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Human movement studies and analyses have been fundamental in many scientific domains, ranging from neuroscience to education, pattern recognition to robotics, health care to sports, and beyond. Previous speech motor models were proposed to understand how speech movement is produced and how the resulting speech varies when some parameters are changed. However, the inverse approach, in which the muscular response parameters and the subject’s age are derived from real continuous speech, is not possible with such models. Instead, in the handwriting field, the kinematic theory of rapid human movements and its associated Sigma-lognormal model have been applied successfully to obtain the muscular response parameters. This work presents a speech kinematics-based model that can be used to study, analyze, and reconstruct complex speech kinematics in a simplified manner. A method based on the kinematic theory of rapid human movements and its associated Sigma-lognormal model are applied to describe and to parameterize the asymptotic impulse response of the neuromuscular networks involved in speech as a response to a neuromotor command. The method used to carry out transformations from formants to a movement observation is also presented. Experiments carried out with the (English) VTR-TIMIT database and the (German) Saarbrucken Voice Database, including people of different ages, with and without laryngeal pathologies, corroborate the link between the extracted parameters and aging, on the one hand, and the proportion between the first and second formants required in applying the kinematic theory of rapid human movements, on the other. The results should drive innovative developments in the modeling and understanding of speech kinematics.

The Kinematic Theory of rapid movements and its associated Sigma-Lognormal model have been extensively used in a large variety of applications. While the physical and biological meaning of the model have been widely tested and validated for rapid movements, some shortcomings have been detected when it is used with continuous long and complex movements. To alleviate such drawbacks, and inspired by the motor equivalence theory and a conceivable visual feedback, this paper proposes a novel framework to extract the Sigma-Lognormal parameters, namely iDeLog. Specifically, iDeLog consists of two steps. The first one, influenced by the motor equivalence model, separately derives an initial action plan defined by a set of virtual points and angles from the trajectory and a sequence of lognormals from the velocity. In the second step, based on a hypothetical visual feedback compatible with an open-loop motor control, the virtual target points of the action plan are iteratively moved to improve the matching between the observed and reconstructed trajectory and velocity. During experiments conducted with handwritten signatures, iDeLog obtained promising results as compared to the previous development of the Sigma-Lognormal.

Computer aided diagnosis systems can provide non-invasive, low-cost tools to support clinicians. These systems have the potential to assist the diagnosis and monitoring of neurodegenerative disorders, in particular Parkinson’s disease (PD). Handwriting plays a special role in the context of PD assessment. In this paper, the discriminating power of “dynamically enhanced” static images of handwriting is investigated. The enhanced images are synthetically generated by exploiting simultaneously the static and dynamic properties of handwriting. Specifically, we propose a static representation that embeds dynamic information based on: (i) drawing the points of the samples, instead of linking them, so as to retain temporal/velocity information; and (ii) adding pen-ups for the same purpose. To evaluate the effectiveness of the new handwriting representation, a fair comparison between this approach and state-of-the-art methods based on static and dynamic handwriting is conducted on the same dataset, i.e. PaHaW. The classification workflow employs transfer learning to extract meaningful features from multiple representations of the input data. An ensemble of different classifiers is used to achieve the final predictions. Dynamically enhanced static handwriting is able to outperform the results obtained by using static and dynamic handwriting separately.

Building upon findings in computational model of handwriting learning and execution, we introduce the concept of stability to explain the difference between the actual movements performed during multiple execution of the subject’s signature, and conjecture that the most stable parts of the signature should play a paramount role in evaluating the similarity between a questioned signature and the reference ones during signature verification. We then introduce the Stability Modulated Dynamic Time Warping algorithm for incorporating the stability regions, i.e. the most similar parts between two signatures, into the distance measure between a pair of signatures computed by the Dynamic Time Warping for signature verification. Experiments were conducted on two datasets largely adopted for performance evaluation. Experimental results show that the proposed algorithm improves the performance of the baseline system and compares favourably with other top performing signature verification systems.

Online fraud often involves identity theft. Since most security measures are weak or can be spoofed, we investigate a more nuanced and less explored avenue: behavioral biometrics via handwriting movements. This kind of data can be used to verify whether a user is operating a device or a computer application, so it is important to distinguish between human and machine-generated movements reliably. For this purpose, we study handwritten symbols (isolated characters, digits, gestures, and signatures) produced by humans and machines, and compare and contrast several deep learning models. We find that if symbols are presented as static images, they can fool state-of-the-art classifiers (near 75% accuracy in the best case) but can be distinguished with remarkable accuracy if they are presented as temporal sequences (95% accuracy in the average case). We conclude that an accurate detection of fake movements has more to do with how users write, rather than what they write. Our work has implications for computerized systems that need to authenticate or verify legitimate human users, and provides an additional layer of security to keep attackers at bay.

The paper presents a summary of the 1st Competition on Script Identification in the Wild (SIW 2021) organised in conjunction with 16th International Conference on Document Analysis and Recognition (ICDAR 2021). The goal of SIW is to evaluate the limits of script identification approaches through a large scale in the wild database including 13 scripts (MDIW-13 dataset) and two different scenarios (handwritten and printed). The competition includes the evaluation over three different tasks depending of the nature of the data used for training and testing. Nineteen research groups registered for SIW 2021, out of which 6 teams from both academia and industry took part in the final round and submitted a total of 166 algorithms for scoring. Submissions included a wide variety of deep-learning solutions as well as approaches based on standard image processing techniques. The performance achieved by the participants prove the elevate accuracy of deep learning methods in comparison with traditional statistical approaches. The best approach obtained classification accuracies of 99% in all three tasks with experiments over more than 50K test samples. The results suggest that there is still room for improvements, specially over handwritten samples and specific scripts.

This paper describes the experimental framework and results of the ICDAR 2021 Competition on On-Line Signature Verification (SVC 2021). The goal of SVC 2021 is to evaluate the limits of on-line signature verification systems on popular scenarios (office/mobile) and writing inputs (stylus/finger) through large-scale public databases. Three different tasks are considered in the competition, simulating realistic scenarios as both random and skilled forgeries are simultaneously considered on each task. The results obtained in SVC 2021 prove the high potential of deep learning methods. In particular, the best on-line signature verification system of SVC 2021 obtained Equal Error Rate (EER) values of 3.33% (Task 1), 7.41% (Task 2), and 6.04% (Task 3).

Run-length features have shown its effectivity in off-line signature verification. Configurations of these handcrafted features include four direction-based features. In this paper, we propose to add further spatial information of the signature to the standard run-length features. Such information is worked out in two stages: firstly, beyond classical four directions, more directions are studied. Secondly, improving the knowledge of each direction by combining the information of the neighbor directions. This new configuration has been used in two classifiers, one based on Euclidean distance and another based on a one-class support vector machine. We outperformed previous performances with the proposed configuration regarding the last ICFHR 2018 competition on Thai student signatures and GPDS-960 signature database.

© The Institution of Engineering and Technology 2018. This study focuses on a comprehensive study of Automatic Signature Verification (ASV) for off-line Thai signatures; an investigation was carried out to characterise the challenges in Thai ASV and to baseline the performance of Thai ASV employing baseline features, being Local Binary Pattern, Local Directional Pattern, Local Binary and Directional Patterns combined (LBDP), and the baseline shape/feature-based hidden Markov model. As there was no publicly available Thai signature database found in the literature, the authors have developed and proposed a database considering real-world signatures from Thailand. The authors have also identified their latent challenges and characterised Thai signature-based ASV. The database consists of 5,400 signatures from 100 signers. Thai signatures could be bi-script in nature, considering the fact that a single signature can contain only Thai or Roman characters or contain both Roman and Thai, which poses an interesting challenge for script-independent SV. Therefore, along with the baseline experiments, the investigation on the influence and nature of bi-script ASV was also conducted. From the equal error rates and Bhattacharyya distance, the score achieved in the experiments indicate that the Thai SV scenario is a script-independent problem. The open research area on this subject of research has also been addressed.

The dynamic signature is a biometric trait widely used and accepted for verifying a person's identity. Current automatic signature-based biometric systems typically require five, ten, or even more specimens of a person's signature to learn intrapersonal variability sufficient to provide an accurate verification of the individual's identity. To mitigate this drawback, this paper proposes a procedure for training with only a single reference signature. Our strategy consists of duplicating the given signature a number of times and training an automatic signature verifier with each of the resulting signatures. The duplication scheme is based on a sigma lognormal decomposition of the reference signature. Two methods are presented to create human-like duplicated signatures: the first varies the strokes' lognormal parameters (stroke-wise) whereas the second modifies their virtual target points (target-wise). A challenging benchmark, assessed with multiple state-of-the-art automatic signature verifiers and multiple databases, proves the robustness of the system. Experimental results suggest that our system, with a single reference signature, is capable of achieving a similar performance to standard verifiers trained with up to five signature specimens.

With the overall aim of improving the synthesis of handwritten signatures, we have studied how muscle activation depends on handwriting style for both text and flourish. Surface electromyographic (EMG) signals from a set of twelve arm and trunk muscles were recorded in synchronization with handwriting produced on a digital Tablet. Correlations between these EMG signals and handwritten trajectory signals were analyzed so as to define the sequence of muscles activated during the different parts of the signature. Our results establish a correlation between the speed of the movement, stroke size, handwriting style and muscle activation. Muscle activity appeared to be clustered as a function of movement speed and handwriting style, a finding which may be used for filter design in a signature synthesizer.

Novel Stability-based system for early bearing fault detection.Novel use of Dynamic Time Warping and Direct Matching Point techniques.Findings on continuous monitoring of vibration signals with low false alarms for faultsFindings on two publicly available databases under several machining conditions. This paper presents a new and straightforward system for bearing fault detection. The system computes the stability of two vibration signals by using the direct matching points (DMP) of an elastic and non-linear align function. It is able to find discriminant properties in the stability of fault-free and faulty bearing vibration signals from the early and late stages of the fault in critical bearing parts. Because training data constitutes one of the critical challenges in most expert and intelligent systems, one of the novelties of the proposed stability-based system is that it requires neither training nor fine-tuning. A significant impact on the robustness of the system is demonstrated using two publicly available vibration signal databases under several load conditions, with real faults, during multiple machine working states. Experimental results validate the use of the proposed stability-based system for predictive maintenance in bearings.

Method of synthesizing the temporal evolution of handwriting from childhood to adulthood.Synthesis of both online and offline handwriting.Parameters (E, D,t,K) for dealing with synthesized handwriting evolution.Method for comparing temporal evolution of real and synthetic handwriting. New methods for generating synthetic handwriting images for biometric applications have recently been developed. The temporal evolution of handwriting from childhood to adulthood is usually left unexplored in these works. This paper proposes a novel methodology for including temporal evolution in a handwriting synthesizer by means of simplifying the text trajectory plan and handwriting dynamics. This is achieved through a tailored version of the kinematic theory of rapid human movements and the neuromotor inspired handwriting synthesizer. The realism of the proposed method has been evaluated by comparing the temporal evolution of real and synthetic samples both quantitatively and subjectively. The quantitative test is based on a visual perception algorithm that compares the letter variability and the number of strokes in the real and synthetic handwriting produced at different ages. In the subjective test, 30 people are asked to evaluate the perceived realism of the evolution of the synthetic handwriting.

The synthetic generation of static handwritten signatures based on motor equivalence theory has been recently proposed for biometric applications. Motor equivalence divides the human handwriting action into an effector dependent cognitive level and an effector independent motor level. The first level has been suggested by others as an engram, generated through a spatial grid, and the second has been emulated with kinematic filters. Our paper proposes a development of this methodology in which we generate dynamic information and provide a unified comprehensive synthesizer for both static and dynamic signature synthesis. The dynamics are calculated by lognormal sampling of the 8-connected continuous signature trajectory, which includes, as a novelty, the pen-ups. The forgery generation imitates a signature by extracting the most perceptually relevant points of the given genuine signature and interpolating them. The capacity to synthesize both static and dynamic signatures using a unique model is evaluated according to its ability to adapt to the static and dynamic signature inter- and intra-personal variability. Our highly promising results suggest the possibility of using the synthesizer in different areas beyond the generation of unlimited databases for biometric training.

Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and as far as we know based on heuristic affine transforms and does not seem to consider the recent advances in human behavior modeling of neuroscience. This paper tries to fill this gap by proposing a cognitive inspired algorithm to duplicate off-line signatures. The algorithm is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process. The duplicator is evaluated by increasing artificially a training sequence and verifying that the performance of four state-of-the-art off-line signature classifiers using two publicly databases have been improved on average as if we had collected three more real signatures.

Improving the ability of forgery detection in Automatic Signature Verifiers requires databases with an extensive and realistic set of skilled forgeries. However, collecting skilled professional copies is not always possible in many studies, and most the publicly available databases are limited from that point of view. Recently, the Kinematic Theory of Rapid Movements has been used to improve the skillfulness from a given on-line skilled forgery. This paper proposes a method based on the Kinematic Theory of Rapid Movements to generate both on-line and off-line skilled synthetic forgeries from a single on-line genuine specimen. The method consists of three steps: 1. The genuine on-line signature is decomposed as a sum of overlapped lognormal strokes, 2. The trajectory is modified by distorting the virtual target points and the lognormal parameters, and 3. A new velocity profile is automatically synthetized from the 8-connected modified trajectory. Experiments with multiple on-line and off-line signature verifiers show that this method can generate skilled forgeries harder to detect.

The paper presents an approach to design rapid and fluid movements of an universal robot to perform robot writing mimicking both the human kinematics when signing and the trajectory. To perform the task, on-line human signing standards are created first. Robot writing task is performed using these standards after that and robot signatures are acquired as a result. Next, both human and robot signatures are analyzed and compared, and the degree of signatures correlation is defined. Finally, recommendations of robot motion improvement are given.

A new framework for liveness detection at the intra class level/user level.Proposed a more realistic database for liveness detection of ocular biometrics.A set of image quality features are proposed for liveness detection in ocular biometrics.To demonstrate the proposed framework, versatile spoofing techniques were explored.The proposed framework is more realistic and efficient compared to the state-of-the-art. In this research a new framework for software-based liveness detection for direct attacks in multimodal ocular biometrics across the visible spectrum is proposed. The framework aims to develop a more realistic method for liveness detection compared to previous frameworks proposed in the literature. To fulfil the above highlighted aims in this framework, intra-class level (i.e. user level) liveness detection is introduced. To detect liveness, a new set of image quality-based features is proposed for multimodal ocular biometrics in the visible spectrum. A variety of transformed domain (focus related) aspect and contrast-related quality features are employed to design the framework. Furthermore a new database is developed for liveness detection of multimodal ocular biometrics, which has the prominent presence of multimodal ocular traits (both sclera and iris). Moreover this database is comprised of a larger variety of fake images; those were prepared by employing versatile forging techniques which can be exhibited by imposters. Therefore the proposed schema has dealt with versatile categories of spoofing methods, which were not considered previously in the literature. The database contains a set of 500 fake and 500 genuine eye images acquired from 50 different eyes. An appreciable liveness detection result is achieved in the experiments. Furthermore, the experimental results conclude that this new framework is more efficient and competitive when compared to previous liveness detection schemes. Display Omitted

Handwritten signature datasets are really necessary for the purpose of developing and training automatic signature verification systems. It is desired that all samples in a signature dataset should exhibit both inter-personal and intra-personal variability. A possibility to model this reality seems to be obtained through the synthesis of signatures. In this paper we propose a method based on motor equivalence model theory to generate static Bengali signatures. This theory divides the human action to write mainly into cognitive and motor levels. Due to difference between scripts, we have redesigned our previous synthesizer [1,2], which generates static Western signatures. The experiments assess whether this method can approach the intra and inter-personal variability of the Bengali-100 Static Signature DB from a performance-based validation. The similarities reported in the experimental results proof the ability of the synthesizer to generate signature images in this script.

Many features have been proposed in on-line signature verification. Generally, these features rely on the position of the on-line signature samples and their dynamic properties, as recorded by a tablet. This paper proposes a novel feature space to describe efficiently on-line signatures. Since producing a signature requires a skeletal arm system and its associated muscles, the new feature space is based on characterizing the movement of the shoulder, the elbow and the wrist joints when signing. As this motion is not directly obtained from a digital tablet, the new features are calculated by means of a virtual skeletal arm (VSA) model, which simulates the architecture of a real arm and forearm. Specifically, the VSA motion is described by its 3D joint position and its joint angles. These anthropomorphic features are worked out from both pen position and orientation through the VSA forward and direct kinematic model. The anthropomorphic features’ robustness is proved by achieving state-of-the-art performance with several verifiers and multiple benchmarks on third party signature databases, which were collected with different devices and in different languages and scripts.

This paper proposes a novel set of function-based features for dynamic signature verification. They are inspired on the human stance and the variations in the angles of the arm joints when signing. Specifically, we propose to convert the trajectory and pen-tip altitude and azimuth from an on-line signature into the required sequence of an anthropomorphic robot poses to reproduce such signature. Then, these new robotic sequences are evaluated in an on-line automatic signature verifier. The robotic arm poses are defined by the angles of its joints. These values are worked out by means of the homogeneous transformation matrices between the different coordinate frames of each joint by using the Denavit-Hartenberg (DH) parameterization. The sequences of joint angles represent the new function-based feature space that we propose for signature verification. Our experimental results on the MCYT-100 corpus highlight the effectiveness of robotic-based features in signature verification. Moreover, competitive performances are achieved in random and skilled forgery experiments, compared to previous approaches.

© 2018 IEEE. This paper summarises the results of the Sclera Segmentation Benchmarking Competition (SSBC 2018). It was organised in the context of the 11th IAPR International Conference on Biometrics (ICB 2018). The aim of this competition was to record the developments on sclera segmentation in the cross-sensor environment (sclera trait captured using multiple acquiring sensors). Additionally, the competition also aimed to gain the attention of researchers on this subject of research. For the purpose of benchmarking, we have developed two datasets of sclera images captured using different sensors. The first dataset was collected using a DSLR camera and the second one was collected using a mobile phone camera. The first dataset is the Multi-Angle Sclera Dataset (MASD version 1), which was used in the context of the previous versions of sclera segmentation competitions. The images in the second dataset were captured using.a mobile phone rear camera of 8-megapixel. As baseline manual segmentation mask of the sclera images from both the datasets were developed. Precision and recall-based statistical measures were employed to evaluate the effectiveness of the submitted segmentation technique and to rank them. Six algorithms were submitted towards the segmentation task. This paper analyses the results produced by these algorithms/system and defines a way forward for this subject of research. Both the datasets along with some of the accompanying ground truth/baseline mask will be freely available for research purposes upon request to authors by email.

The use of robotics arms provides students several advantages in the teaching and learning process, compared to methods based on simulation programs. Moreover, programming the robotic arm to imitate a human action previously carried out by the students, makes them to have a better personal satisfaction. Following up this idea, a lab session has been designed and tested by the authors. It is composed by the following phases: Students first register their personal handwriting on a digitizing tablet. Next, they develop a program to make that a commercial robot writes such piece of handwriting. Then, the commercial robotic arm produce such piece of handwriting on a digitizing tablet. Finally, a comparison between the original handwriting and the one made by the robot is worked out. To evaluate the success of the proposed lab session, a survey was issued to students who followed the course in robotics last semester with encouraging resuts.

First experimental study on the ridge pattern distribution in the 'interdigital palm region' for biometrics.Novel classification methodology of palms according to five classes.Study of complementarity of the interdigital and traditional palm regions.Evaluation of personal recognition using interdigital region reaching EER = 0.01% (on database on 416 subjects).Interdigital palm region image database from 416 subjects in this paper is made publicly available. The interdigital palm region represents about 30% of the palm area and is inherently acquired during palmprint imaging, nevertheless it has not yet attracted any noticeable attention in biometrics research. This paper investigates the ridge pattern characteristics of the interdigital palm region for its usage in biometric identification. An anatomical study of the interdigital area is initially carried out, leading to the establishment of five categories according to the distribution of the singularities and three regions of interest for biometrics. With the identified regions, our study analyzes the matching performance of the interdigital palm biometrics and its combination with the conventional palmprint matching approaches and presents comparative experimental results using four competing feature extraction methods. This study has been carried out with two publicly available databases. The first one consists of 2,080 images of 416 subjects acquired with a touchless low-cost imaging device focused on acquiring the interdigital palm area. The second database is the publicly available BiosecurID hand database which consists of 3,200 images from 400 users. The experimental results presented in this paper suggest that features from the interdigital palm region can be used to achieve competitive performances as well as offer significant improvements for conventional palmprint recognition.

This is the author’s version of a work that was accepted for publication in Pattern Recognition . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition , 48, 9 (2005) DOI: 10.1016/j.patcog.2015.03.019, On-line signature verification still remains a challenging task within biometrics. Due to their behavioral nature (opposed to anatomic biometric traits), signatures present a notable variability even between successive realizations. This leads to higher error rates than other largely used modalities such as iris or fingerprints and is one of the main reasons for the relatively slow deployment of this technology. As a step towards the improvement of signature recognition accuracy, the present paper explores and evaluates a novel approach that takes advantage of the performance boost that can be reached through the fusion of on-line and off-line signatures. In order to exploit the complementarity of the two modalities, we propose a method for the generation of enhanced synthetic static samples from on-line data. Such synthetic off-line signatures are used on a new on-line signature recognition architecture based on the combination of both types of data: real on-line samples and artificial off-line signatures synthesized from the real data. The new on-line recognition approach is evaluated on a public benchmark containing both real versions (on-line and off-line) of the exact same signatures. Different findings and conclusions are drawn regarding the discriminative power of on-line and off-line signatures and of their potential combination both in the random and skilled impostors scenarios., M. D.-C. is supported by a PhD fellowship from the ULPGC and M.G.-B. is supported by a FPU fellowship from the Spanish MECD. This work has been partially supported by projects: MCINN TEC2012-38630- C04-02, Bio-Shield (TEC2012-34881) from Spanish MINECO, BEAT (FP7-SEC-284989) from EU, CECABANK and Cátedra UAM-Telefónica

In this paper we propose a new method for generating synthetic handwritten signature images for biometric applications. The procedures we introduce imitate the mechanism of motor equivalence which divides human handwriting into two steps: the working out of an effector independent action plan and its execution via the corresponding neuromuscular path. The action plan is represented as a trajectory on a spatial grid. This contains both the signature text and its flourish, if there is one. The neuromuscular path is simulated by applying a kinematic Kaiser filter to the trajectory plan. The length of the filter depends on the pen speed which is generated using a scalar version of the sigma lognormal model. An ink deposition model, applied pixel by pixel to the pen trajectory, provides realistic static signature images. The lexical and morphological properties of the synthesized signatures as well as the range of the synthesis parameters have been estimated from real databases of real signatures such as the MCYT Off-line and the GPDS960GraySignature corpuses. The performance experiments show that by tuning only four parameters it is possible to generate synthetic identities with different stability and forgers with different skills. Therefore it is possible to create datasets of synthetic signatures with a performance similar to databases of real signatures. Moreover, we can customize the created dataset to produce skilled forgeries or simple forgeries which are easier to detect, depending on what the researcher needs. Perceptual evaluation gives an average confusion of 44.06 percent between real and synthetic signatures which shows the realism of the synthetic ones. The utility of the synthesized signatures is demonstrated by studying the influence of the pen type and number of users on an automatic signature verifier.

The aim of this paper is to analyze the improvements that are observed in the glottal excitation synthesizers when the possible manifestations of non-linear behavior are characterized in glottal excitation. This paper proposes a new model based on the modification of a classic glottal excitation synthesizer and to study the improvements regarding different glottal excitation synthesizers. The proposed model tries to improve the naturalness of the synthesized voice using the synthesis of the sub-harmonics. The proposed model is included in a generic synthesizer of sustained vowels in order to get an assessment about the quality of the synthesis of different qualities of voice, where speakers with pathologies in the phonatory system are used to simulate the behavior of low quality voices. The different models are adjusted using genetic algorithms. The assessment of the different glottal excitation synthesizers is obtained using an objective measure of similarity between the original signals and the synthesized signals based on temporal and spectral measurements. In addition, the quality of the proposed glottal excitation model is evaluated with a study of subjective perception.

© 2017 IEEE. This paper summarises the results of the Sclera Segmentation and Eye Recognition Benchmarking Competition (SSERBC 2017). It was organised in the context of the International Joint Conference on Biometrics (IJCB 2017). The aim of this competition was to record the recent developments in sclera segmentation and eye recognition in the visible spectrum (using iris, sclera and peri-ocular, and their fusion), and also to gain the attention of researchers on this subject. In this regard, we have used the Multi-Angle Sclera Dataset (MASD version 1). It is comprised of2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82×2) eyes. A manual segmentation mask of these images was created to baseline both tasks. Precision and recall based statistical measures were employed to evaluate the effectiveness of the segmentation and the ranks of the segmentation task. Recognition accuracy measure has been employed to measure the recognition task. Manually segmented sclera, iris and peri-ocular regions were used in the recognition task. Sixteen teams registered for the competition, and among them, six teams submitted their algorithms or systems for the segmentation task and two of them submitted their recognition algorithm or systems. The results produced by these algorithms or systems reflect current developments in the literature of sclera segmentation and eye recognition, employing cutting edge techniques. The MASD version 1 dataset with some of the ground truth will be freely available for research purposes. The success of the competition also demonstrates the recent interests of researchers from academia as well as industry on this subject.

Tracing complex and long handwritten signatures takes an important role in signature verification. Indeed, whether a dynamic signature could be inferred from its static counterpart, improvements would be expected during the automatic verification. An important factor in recovering the tracing of a thinned signature is the feasible and accurate processing of the clusters. A cluster is produced when two or more pieces of handwriting intertwine. Specifically, the challenge is to find which input branch is associated to which output branch and the path between them inside the cluster. In this paper, a novel proposal, based on good continuity criteria derived from both visual perception and movement execution, is developed to solve the paths within the clusters. To this aim, our implementation focuses on a multiscale analysis of the thinned traces and the Dijkstra's algorithm for an effective branch association. Experiments have been carried out with SigComp2009 and SUSIG-Visual signature databases, which are two publicly available Western-based corpus. Promising results have been obtained in our evaluation when studying the success rate in the branches association. The results confirm that processing clusters is important to detect components and a correct cluster branch association improves the writing order recovery performance. Finally, encouraging results have been obtained when performing a global estimation of the writing order in handwriting signatures, in terms of Root Mean Square Error and Dynamic Time Warping.

Estimating the salient points in 8-connected curves from handwritten signature is a difficult task due to their relation to the writer neuromotor system. This paper faces up this topic proposing a two-steps perceptual important points estimation method: the first step estimates the sharper salient points by a curvature analysis at multiple scales, whereas the second step estimates the smoother salient points relying on circular shapes between estimated salient points in step one. In this approach, both sharper and smoother salient points represent the set of perceptual important points in an eight connected signature trajectory. Our validations, conducted on 2112 signatures from 132 users of the BiosecurID database, are focused on i) evaluating the number of estimated perceptual important points; ii) evaluating their locations in the trajectory and iii) evaluating the accuracy of the estimated duration of the signatures from the number of perceptual important points. The obtained results are encouraging for new developments in handwriting analysis based on this procedure.

© 2017 IEEE. This paper discusses the possibility of use of some challenging face images scenario captured from optical phenomenon in the wild for forensic purpose towards individual identification. Occluded and under cover face images in surveillance scenario can be collected from its reflection on a surrounding glass or on a smooth wall that is under the coverage of the surveillance camera and such scenario of face images can be linked for forensic purposes. Another similar scenario that can also be used for forensic is the face images of an individual standing behind a transparent glass wall. To investigate the capability of these images for personal identification this study is conducted. This work investigated different types of features employed in the literature to establish individual identification by such degraded face images. Among them, local region based featured worked best. To achieve higher accuracy and better facial features face image were cropped manually along its close bounding box and noise removal was performed (reflection, etc.). In order to experiment we have developed a database considering the above mentioned scenario, which will be publicly available for academic research. Initial investigation substantiates the possibility of using such face images for forensic purpose.

Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the motor equivalence model, the well-established hypothesis from neuroscience, which analyses how a human being accomplishes movement. Specifically, a motor equivalence model divides human actions into two steps: 1) the effector independent step at cognitive level and 2) the effector dependent step at motor level. In fact, recent work reports the successful application to Western scripts of a handwriting synthesizer, based on this theory. This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For this purpose, we use two different online and offline databases for both Bengali and Devanagari signatures. This paper reports an effective synthesizer for static and dynamic signatures written in Devanagari or Bengali scripts. We obtain promising results with artificially generated signatures in terms of appearance and performance when we compare the results with those for real signatures.

This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 238 (2014) DOI: 10.1016/j.ins.2013.06.015, The present work proposes a novel probabilistic method to reconstruct a hand shape image from its template. We analyse the degree of similarity between the reconstructed images and the original samples in order to determine whether the synthetic hands are able to deceive hand recognition systems. This analysis is made through the estimation of the success chances of an attack carried out with the synthetic samples against an independent system. The experimental results show that there is a high chance of breaking a hand recognition system using this approach. Furthermore, since it is a probabilistic method, several synthetic images can be generated from each original sample, which increases the success chances of the attack., This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Challenge (TEC2009-11186), BIOSINT (TEC2012-38630-C04-02) and Bio-Shield (TEC2012-34881) from Spanish MINECO, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica. Marta Gomez-Barrero is supported by a FPU Fellowship from Spanish MECD.

Hand based biometry includes some of the most useful technologies for person identification. The search for new techniques, which complement the battery of existing methods, is an open topic. This paper examines the utility of hyperspectral imagery for hand recognition. Hyperspectral technology permits the sensing of the subsurface tissue structure, which is significantly different from person to person. The data are collected using a SWIR camera in conjunction with an optical spectrograph. This transforms the camera into a line-scan hyperspectral imaging device. Three feature extraction methods for hyperspectral hand curve characterization are examined. They are based on the area, slope or curvature at different automatically selected spatial hand positions. We report a set of experiments which explore: best hand zones for extracting local hyperspectral features; robustness against the number of training samples; error detection; and occlusion. Different strategies for combining the spectral features with geometric traits available in the hyperspectral cube are discussed. Our experiments show that local spectral properties of human tissue are effective discriminants for biometric recognition with a performance near to or better than that obtained by other hand traits. Equal Error Rates of 0.05% and an identification rate of 96.71% are obtained from a database of 154 people. These results along with their higher robustness to spoofing attacks make the hyperspectral features a promising alternative for person identification.

This paper proposes the application of complexity measures based on nonlinear dynamics for emotional speech characterization. Measures such as mutual information, dimension correlation, entropy correlation, Shannon entropy, Lempel-Ziv complexity and Hurst exponent are extracted from the samples of three databases of emotional speech. Then, statistics such as mean, standard deviation, skewness and kurtosis are applied on the extracted measures. Experiments were conducted on the Polish emotional speech database, on the Berlin emotional speech database and on the LCD emotional database for a three-class problem (neutral, fear and anger emotional states). A procedure for feature selection is proposed based on an affinity analysis of the features. This feature selection procedure is accomplished to select a reduced number of features over the Polish emotional database. Finally, the selected features are evaluated in the Berlin emotional speech database and in the LDC emotional database using a neural network classifier in order to assess the usefulness of the selected features. Global success rates of 72.28%, 75.4% and 80.75%, were obtained for the Polish emotional speech database, the Berlin emotional speech database and the LDC emotional speech database respectively.

Condition monitoring of rotating machines is important in the prevention of failures. As most machine malfunctions are related to bearing failures, several bearing diagnosis techniques have been developed. Some of them feature the bearing vibration signal with statistical measures and others extract the bearing fault characteristic frequency from the AM component of the vibration signal. In this paper, we propose to transform the vibration signal to the Teager-Kaiser domain and feature it with statistical and energy-based measures. A bearing database with normal and faulty bearings is used. The diagnosis is performed with two classifiers: a neural network classifier and a LS-SVM classifier. Experiments show that the Teager domain features outperform those based on the temporal or AM signal.

Segmenting and tracking of people is an important aim in the video analysis with multitudinous applications. The scene calibration enables the system to process the input video in a different way depending on the camera position and the scene characteristics for arising the successful results. In complex situations there exists extended occlusions, shadows and/or rejections, so that an appropriate calibration is required in order to achieve a highly developed people's segmentation as well as a tracking algorithm. In the majority of cases, once the system has been installed in a certain scene, it is difficult to obtain the calibration information of the scene. In this paper, an automatic method to calibrate the scene for detecting and tracking people systems is presented based on measurements of video sequences captured from a stationary camera.

© 2017 IEEE. This research proposed an automatic student identification and verification system utilising off-line Thai name components. The Thai name components consist of first and last names. Dense texture-based feature descriptors were able to yield encouraging results when applied to different handwritten text recognition scenarios. As a result, the authors employed such features in investigating their performance on Thai name component verification system. In this research, Dense-Local Binary Pattern, Dense-Local Directional Pattern, and Local Binary Pattern combined with Local Directional Pattern were employed. A base-line shape/feature i.e. Hidden Markov Model (HMM) was also utilised in this study. As there is no dataset on Thai name verification in the literature, a dataset is proposed for a Thai name verification system. The name component samples were collected from high school students. It consists of 8,400 name components (first and last names) from 100 students. Each student provided 60 genuine name components, and each of the name components was forged by 12 other students. An encouraging result was found employing the above-mentioned features on the proposed dataset.

This article propose a complete framework to recover the dynamic properties (i.e. velocity and pressure) of an on-line Western signature from an image-based signature. The framework is based on classical approaches to recover the writing order of the strokes and a novel process to recover the kinematic properties from thinned trajectories. In order to evaluate the quality of the recovered signatures and the impact of each stage of our framework, the performance of a signature verification system on obtained signatures in each stage are compared to the performance with real signatures. As a proof of concepts, in this study we use the first 50 users of BiosecurID signature database since they contain both the on-line and off-line version of Western signatures.

© Springer Science+Business Media Singapore 2017. This work proposes a sclera vessel texture pattern synthesis technique. Sclera texture was synthesized by a non-parametric based texture regeneration technique. A small number of classes from the UBIRIS version: 1 dataset was employed as primitive images. An appreciable result was achieved which solicits the successful synthesis of sclera texture patterns. It is difficult to get a huge collection real sclera data and hence such synthetic data will be useful to the researchers.

As an emerging issue, multi-script signature verification is a recent challenge for current Automatic Signature Verification (ASV) systems. Relevant differences are presented in the morphology and lexicon of the signature images written in different scripts, such as used symbols, shape of the signatures, legibility, etc. These peculiarities could reduce the success of ASV systems, especially those which were originally designed for only one kind of script. However, one common feature among scripts in ASV is the fact that the greater the number of signatures that are used for training, the better the expected performance. In this work, we propose a method inspired by observations from the neuromotor equivalence theory to artificially enlarge the signature images used to train a state-of-the-art static signature classifier. Experimental results are obtained by using three static signature datasets derived from completely different scripts: Western, Bengali and Devanagari. Our results suggest that the cognitive-inspired model, which aims to duplicate static signatures, tends toward intra-class variability of signatures written in different scripts; the model's beneficial impact is seen in signature verification tests.

© 2016 IEEE. This work proposes a projective pairwise dictionary learning-based approach for fast and efficient multimodal eye biometrics. The work uses a faster Projective pairwise Discriminative Dictionary Learning (DL) in contrast to the traditional DL which uses synthesis DL. Projective Pairwise Discriminative Dictionary (PPDD) uses a synthesis dictionary and an analysis dictionary jointly to achieve the goal of pattern representation and discrimination. As the PPDD process of DL is in contrast to the use of l0 or l1-norm sparsity constraints on the representation coefficients adopted in most traditional DL, it works faster than other DL. Moreover, the blending of synthesis dictionary and an analysis dictionary also enhance the feature representation of the complex eye patterns. We employed the combination of sclera and iris traits to establish multimodal biometrics. The experimental study and analysis conducted fulfill the hypothesis we considered. In this work we employed a part of the UBIRIS version 1 dataset to conduct the experiments.

© 2016 IEEE. This article reports and summarizes the results of a competition on sclera segmentation and recognition benchmarking, called Sclera Segmentation and Recognition Benchmarking Competition 2016 (SSRBC 2016). It was organized in the context of the 9th IAPR International Conference on Biometrics (ICB 2016). The goal of this competition was to record the recent developments in sclera segmentation and recognition, and also to gain the attention of researchers on this subject of biometrics. In this regard, we have used a multi-angle sclera dataset (MASD version 1). It is comprised of 2624 images taken from both the eyes of 82 identities. Therefore, it consists of images of 164 (82∗2) different eyes. We have prepared a manual segmentation mask of these images to create the baseline for both tasks. We have, furthermore, adopted precision and recall based statistical measures to evaluate the effectiveness of the segmentation and the ranks of the competing algorithms. The recognition accuracy measure has been employed to measure the recognition task. To summarize, twelve participants registered for the competition, and among them, three participants submitted their algorithms/ systems for the segmentation task and two their recognition algorithm. The results produced by these algorithms reflect developments in the literature of sclera segmentation and recognition, employing cutting edge segmentation techniques. Along with the algorithms of three competing teams and their results, the MASD version 1 dataset will also be freely available for research purposes from the organizer's website. The competition also demonstrates the recent interests of researchers from academia as well as industry on this subject of biometrics.

Spoofing is a real concern with regard to the security of biometric systems, especially in personal identification systems based on contactless hand biometrics which can be easily fooled with a simple hand picture. This study proposes a new approach to increase the security of contactless biometric devices using a skin detector based on reflectance spectroscopy in visual, near infrared and short wave infrared bands. The proposal is evaluated with a set of genuine and synthetic hands made from different potentially spoofing materials.

Several papers have recently appeared in the literature which propose pseudo-dynamic features for automatic static handwritten signature verification based on the use of gray level values from signature stroke pixels. Good results have been obtained using rotation invariant uniform local binary patterns LBP8,1riu2 plus LBP16,2riu2 and statistical measures from gray level co-occurrence matrices (GLCM) with MCYT and GPDS offline signature corpuses. In these studies the corpuses contain signatures written on a uniform white “nondistorting” background, however the gray level distribution of signature strokes changes when it is written on a complex background, such as a check or an invoice. The aim of this paper is to measure gray level features robustness when it is distorted by a complex background and also to propose more stable features. A set of different checks and invoices with varying background complexity is blended with the MCYT and GPDS signatures. The blending model is based on multiplication. The signature models are trained with genuine signatures on white background and tested with other genuine and forgeries mixed with different backgrounds. Results show that a basic version of local binary patterns (LBP) or local derivative and directional patterns are more robust than rotation invariant uniform LBP or GLCM features to the gray level distortion when using a support vector machine with histogram oriented kernels as a classifier.

In this paper, we present a novel approach to person verification by fusing face and lip features. Specifically, the face is modeled by the discriminative common vector and the discrete wavelet transform. Our lip features are simple geometric features based on a lip contour, which can be interpreted as multiple spatial widths and heights from a center of mass. In order to combine these features, we consider two simple fusion strategies: data fusion before training and score fusion after training, working with two different face databases. Fusing them together boosts the performance to achieve an equal error rate as low as 0.4% and 0.28%, respectively, confirming that our approach of fusing lips and face is effective and promising.

A method for conducting off-line handwritten signature verification is described. It works at the global image level and measures the grey level variations in the image using statistical texture features. The co-occurrence matrix and local binary pattern are analysed and used as features. This method begins with a proposed background removal. A histogram is also processed to reduce the influence of different writing ink pens used by signers. Genuine samples and random forgeries have been used to train an SVM model and random and skilled forgeries have been used for testing it. Results are reasonable according to the state-of-the-art and approaches that use the same two databases: MCYT-75 and GPDS-100 Corpuses. The combination of the proposed features and those proposed by other authors, based on geometric information, also promises improvements in performance.

The earmarks are usual evidences in many real criminal investigations. The earprint appears for example when a criminal tries to listen through a window or a door before entering, and the methods to make it visible are similar to those used in latent fingerprint lifting. However, its acceptance as evidence in real prosecutions still raises doubts. Although it is well-accepted the uniqueness of the ear and its usefulness for person identification, the permanence of such discriminate ability in earprints is not obvious. Although the earprints do not have a powerful distinctiveness information, they are useful in a bag of evidences, being a promising soft biometric. This paper explores the discriminant properties of local descriptors for earprint-based automatic biometric recognition systems. The literature has focused on automatic systems based on the global aspect of the images, however scarcely studies have coped with in the well-known discriminate ability of earprint local characteristics. The experiments using more than 6000 images from 1200 people suggest a promising performance in comparison with previous existing proposals based on global features and encourage to further explore in this new soft biometric traits.

What can be done with only one enrolled real hand-written signature in Automatic Signature Verification (ASV)? Using 5 or 10 signatures for training is the most common case to evaluate ASV. In the scarcely addressed case of only one available signature for training, we propose to use modified duplicates. Our novel technique relies on a fully neuromuscular representation of the signatures based on the Kinematic Theory of rapid human movements and its Sigma-Lognormal model. This way, a real on-line signature is converted into the Sigma-Lognormal model domain. The model parameters are then varied to generate new duplicated signatures.

In this paper is proposed a first approach of a novel and simple biometric verification system based on the texture of the hand knuckles. A knuckle image with scale, rotation and translation invariance is isolated from the hand recorded image with digital image processing techniques. The wrinkle of the knuckle images are extracted to a black and white image which is used as biometric feature. A hidden Markov model and a support vector machine are proposed as verifiers. A similar equal error rate of 0.094 is reached by both classifiers with our preliminary database consisting of 8 samples of 20 people hand.