Search

Iris-based biometric systems identify individuals by comparing the characteristics of the iris captured by suited sensors. When reflections are present in the iris image, the portion of the iris covered by the reflections should not be considered in the comparison since it may produce erroneous matches. This paper presents an adaptive design methodology for reflection detection and location in iris biometric images based on inductive classifiers, such as neural networks. In particular, this paper proposes a set of features that can be extracted and measured from the iris image and that can effectively be used to achieve an accurate identification of the reflection position using a trained classifier. In addition, the use of radial symmetry transform (RST) is presented to identify the reflections in iris images. The proposed design methodology is general and can be used in any biometric system based on iris images. [ABSTRACT FROM AUTHOR]

The article presents the study which discusses a methodology that effectively cope with the design of an automatic wood types classification system using the fluorescence spectra which is suitable for applications. The study conducted an experimental setup based on a laser source, a spectrometer and a processing system. In Addition, the set of techniques suitable for the extraction of features from spectra is also discussed.

Abstract: The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I). [Copyright &y& Elsevier]

Temporal and spatial locality of the inputs, i.e., the property allowing a classifier to receive the same samples over time—or samples belonging to a neighborhood—with high probability, can be translated into the design of embedded classifiers. The outcome is a computational complexity and power aware design particularly suitable for implementation. A classifier based on the gated-parallel family has been found particularly suitable for exploiting locality properties: Subclassifiers are generally small, independent each other, and controlled by a master-enabling module granting that only a subclassifier is active at a time, the others being switched off. By exploiting locality properties we obtain classifiers with accuracy comparable with the ones designed without integrating locality but gaining a significant reduction in computational complexity and power consumption. [ABSTRACT FROM AUTHOR]

Compared to other well-known biometric technologies based on physiological traits (e.g., fingerprint, iris, and face), heart biometrics are more robust to presentation attacks and are particularly suitable for continuous/periodic recognition. Most studies on heart biometrics concern electrocardiogram (ECG) and photoplethysmogram (PPG). While the reported results are encouraging, to the best of our knowledge, no studies have been conducted on the interoperability between ECG and PPG biometrics. We present a novel method that is capable of performing single-domain and multiple-domain identity verifications for ECG and PPG signals, providing interoperability between the heterogeneous cardiac signals. Our method does not require the computation of any reference/fiducial point and uses a compact representation of the given signals. We propose MultiCardioNet, a novel Siamese neural network trained by using an ad hoc learning algorithm. MultiCardioNet computes a similarity score between two spectrogram-based representations of cardiac signals. Our learning algorithm iteratively computes a balanced subset of genuine and impostor pairs during the training epochs. We performed experiments on a dataset containing 1,008 pairs of ECG and PPG samples, obtaining accuracy comparable to that of the state-of-the-art methods for single-domain scenarios and demonstrating only a relatively small performance decrease in the multiple-domain scenario. [Display omitted] • The first study on the interoperability between ECG and PPG biometrics, based on Siamese CNNs. • Novel CNNs for processing two-dimensional representations of PPG signals. • Ad-hoc algorithm for training Siamese CNNs, suitable for small-size datasets. [ABSTRACT FROM AUTHOR]

For more than a century, the methods for data representation and the exploration of the intrinsic structures of data have developed remarkably and consist of supervised and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high and the data can come in messy, incomplete, unlabeled, or corrupted forms. Consequently, discovering the hidden structure buried inside such data becomes highly challenging. From this perspective, exploratory data analysis plays a substantial role in learning the hidden structures that encompass the significant features of the data in an ordered manner by extracting patterns and testing hypotheses to identify anomalies. Unsupervised generative learning models are a class of machine learning models characterized by their potential to reduce the dimensionality, discover the exploratory factors, and learn representations without any predefined labels; moreover, such models can generate the data from the reduced factors' domain. The beginner researchers can find in this survey the recent unsupervised generative learning models for the purpose of data exploration and learning representations; specifically, this article covers three families of methods based on their usage in the era of big data: blind source separation, manifold learning, and neural networks, from shallow to deep architectures. [ABSTRACT FROM AUTHOR]

• The first literature review on PPG-based biometric recognition approaches. • The paper describes application scenarios and acquisition techniques. • The paper presents a classification of state-of-the-art methods. • The paper summarizes the performance of state-of-the-art methods. • The paper describes open research problems. The wide diffusion of wearable sensors and mobile devices encouraged the study of biometric recognition techniques that require a low level of cooperation from users. Among them, the analysis of cardiac information extracted from plethysmographic (PPG) signals is attracting the research community due to the possibility of performing continuous authentications using low-cost devices that can acquire signals without any action required from the users. Although PPG-based biometric systems are relatively recent technologies, machine learning techniques and deep learning strategies have shown accuracy in heterogeneous application scenarios. This paper presents the first literature review of PPG-based biometric recognition approaches. First, we describe the application contexts suitable for PPG-based biometrics. Second, we analyze the systems in the literature, describe the acquisition sensors, and present a classification of the processing methods. Third, we summarize the available public datasets and the results achieved by recent state-of-the-art approaches. Finally, we analyze the open problems in this research field. [ABSTRACT FROM AUTHOR]

In recent years, impressive performance of deep learning technology has been recognized in synthetic aperture radar (SAR) automatic target recognition (ATR). Since a large amount of annotated data are required in this technique, it poses a trenchant challenge to the issue of obtaining a high recognition rate through less labeled data. To overcome this problem, inspired by the contrastive learning, we proposed a novel framework named batch instance discrimination and feature clustering (BIDFC). In this framework, different from that of the objective of general contrastive learning methods, embedding distance between samples should be moderate because of the high similarity between samples in the SAR images. Consequently, our flexible framework is equipped with adjustable distance between embedding, which we term as weakly contrastive learning. Technically, instance labels are assigned to the unlabeled data in per batch, and random augmentation and training are performed few times on these augmented data. Meanwhile, a novel dynamic-weighted variance loss (DWV loss) function is also posed to cluster the embedding of enhanced versions for each sample. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) database indicate a 91.25% classification accuracy of our method fine-tuned on only 3.13% training data. Even though a linear evaluation is performed on the same training data, the accuracy can still reach 90.13%. We also verified the effectiveness of BIDFC in OpenSarShip database, indicating that our method can be generalized to other data sets. Our code is available at: https://github.com/Wenlve-Zhou/BIDFC-master. [ABSTRACT FROM AUTHOR]

In the field of measuring parameters of mobile communication base station antenna, most of its methods share some deficiencies to a different extent. The traditional ones suffer from inferior efficiency and low safety coefficient while the state-of-the-art ones reveal problems of unsatisfying operational speed and accuracy. To address these problems, a high-speed and high-precision measuring system has been proposed with AntennaNet to speed up instance segmentation. First, the dual attention mechanism module is designed and adopted in AntennaNet, which can promote the accuracy and efficiency of the detection and segmentation of mobile base station antenna, while the PointRend module with down-tilt fitting is also employed to smooth the edges of masks segmented from antenna images and measure the parameters data. Second, transfer learning is introduced to ameliorate overfitting and training difficulty caused by small samples. Third, threshold, pixel coordinates, and least square are applied to ascertain the number of antennas and measure the relevant parameters. Finally, this high speed and high precision measuring system is complete with the combination of android App and UAV. Experimental results fully attest to the preponderance of the proposed method in its significant improvement of segmentation speed, fitting accuracy speed, and fitting accuracy of the antenna, whose relevant parameters of the fitting also conform to industry standards. Our method not only ensures staff safety but also improves social efficiency, which revolutionized the way of measuring antenna parameters of mobile communication base stations. [ABSTRACT FROM AUTHOR]

As an essential element in industrial steel, automatic defect recognition can guarantee the surface quality through focused supervised learning with ample labelled samples. However, defect recognition inevitably features with data‐limiting characteristic under the influence of costly expert labelling. To address this problem, a novel framework, Instance Contrast (InCo), is proposed with the inspiration of contrastive learning. This framework consists of two streams. One with instance labels attributed to the unlabelled data in each batch for classification, which is called Batch Instance Discrimination (BID). The other with different enhanced samples embedding of the same image aggregated by a new function named dynamic weighted variance loss (DWV loss). Therefore, better semantic features can be learned by model due to the moderation of embedding distance between similar steel defect images. Experimental results on the NEU‐CLS database validate that the proposed method achieves 89.86% classification accuracy with only fine‐tuning on the 1:32 training data, outperforming other general contrastive learning methods. [ABSTRACT FROM AUTHOR]

In recent years, facial expression recognition (FER) has becoming a growing topic in computer vision with promising applications on virtual reality and human–robot interaction. Due to the influence of illumination, individual differences, attitude variation, etc., facial expression recognition with robust accuracy in complex environment is still an unsolved problem. Meanwhile, with the wide use of social communication, massive data are uploaded to the Internet; the effective utilization of those data is still a challenge due to noisy label phenomenon in the study of FER. To resolve the above-mentioned problems, firstly, a double active layer-based CNN is established to recognize the facial expression with high accuracy by learning robust and discriminative features from the data, which could enhance the robustness of network. Secondly, an active incremental learning method was utilized to tackle the problem of using Internet data. During the training phase, a two-stage transfer learning method is explored to transfer the relative information from face recognition to FER task to alleviate the inadequate training data in deep convolution network. Besides, in order to make better use of facial expression data from Web site and further improve the FER accuracy, Unconstrained Facial Expression Database from Web site database is built in this paper. Extensive experiments performed on two public facial expression recognition databases FER 2013 and SFEW 2.0 have demonstrated that the proposed scheme outperforms the state-of-the-art methods, which could achieve 67.08% and 51.90%, respectively. [ABSTRACT FROM AUTHOR]

Renewable energy production is constantly growing worldwide, and some countries produce a relevant percentage of their daily electricity consumption through wind energy. Therefore, decision support systems that can make accurate predictions of wind-based power production are of paramount importance for the traders operating in the energy market and for the managers in charge of planning the nonrenewable energy production. In this paper, we present a decision support system that can predict electric power production, estimate a variability index for the prediction, and analyze the wind farm (WF) production characteristics. The main contribution of this paper is a novel system for long-term electric power prediction based solely on the weather forecasts; thus, it is suitable for the WFs that cannot collect or manage the real-time data acquired by the sensors. Our system is based on neural networks and on novel techniques for calibrating and thresholding the weather forecasts based on the distinctive characteristics of the WF orography. We tuned and evaluated the proposed system using the data collected from two WFs over a two-year period and achieved satisfactory results. We studied different feature sets, training strategies, and system configurations before implementing this system for a player in the energy market. This company evaluated the power production prediction performance and the impact of our system at ten different WFs under real-world conditions and achieved a significant improvement with respect to their previous approach. [ABSTRACT FROM AUTHOR]

• Deep-ECG performs identification and verification with remarkable accuracy. • Deep-ECG is the first approach that uses deep CNNs for ECG biometrics. • Deep-ECG can quickly compare binary templates by computing their Hamming distance. • We propose a technique to enlarge ECG datasets for training deep CNNs. Electrocardiographic (ECG) signals have been successfully used to perform biometric recognition in a wide range of applications. However, ECG-based biometric systems are usually less accurate than technologies based on other physiological traits. To increase their performance, it is necessary to study novel approaches. Deep learning methods, like Convolutional Neural Networks (CNNs), can automatically extract distinctive features, and have demonstrated their effectiveness for other biometric systems. In this paper, we present Deep-ECG, a CNN-based biometric approach for ECG signals. To the best of our knowledge, this is the first study in the literature that uses a CNN for ECG biometrics. Deep-ECG extracts significant features from one or more leads using a deep CNN and compares biometric templates by computing simple and fast distance functions, obtaining remarkable accuracy for identification, verification and periodic re-authentication. Furthermore, using a simple quantization procedure, Deep-ECG can obtain binary templates that can facilitate the use of ECG-based biometric systems in conjunction with cryptographic applications. We also propose a simple method to enlarge the training dataset of ECG samples, which can increase the performance of deep neural networks. We performed experiments on large sets of samples acquired in uncontrolled conditions, proving the accuracy and robustness ofDeep-ECG in non-ideal scenarios. Furthermore, we evaluated the performance of Deep-ECG for the PTB Diagnostic ECG Database, obtaining identification accuracy better or comparable to the best performing methods in the literature, also for signals with different characteristics with respect to the ones used to train the CNN. [ABSTRACT FROM AUTHOR]

• A novel encoder combines CNNs branch and MLPs branch in parallel, which is conducive to the extraction of rich local features while simultaneously building global-spatial-dependencies and global-channel-dependencies. Furthermore, a new feature-interaction module is applied to refine the features of MLPs branch and CNNs branch by allowing dynamic exchange of spatial and channel information. • The introduction of auxiliary loss on SDM predictions is conducted to learn the position and shape information of the skin lesions, further highlighting the boundaries of segmentation predictions. • Comprehensive experiments show that CFF-Net achieves state-of-the-art segmentation performance in four public skin lesion datasets (namely ISIC 2016, ISIC 2017, ISIC 2018, PH2), while having comparable parameters. Additionally, we adopt comparison experiments on three public datasets to demonstrate the applicability of CFF-Net for other segmentation tasks. Melanoma is a highly malignant skin tumor. Accurate segmentation of skin lesions from dermoscopy images is pivotal for computer-aided diagnosis of melanoma. However, blurred lesion boundaries, variable lesion shapes, and other interference factors pose a challenge in this regard. This work proposes a novel framework called CFF-Net (Cross Feature Fusion Network) for supervised skin lesion segmentation. The encoder of the network includes dual branches, where the CNNs branch aims to extract rich local features while MLPs branch is used to establish both the global-spatial-dependencies and global-channel-dependencies for precise delineation of skin lesions. Besides, a feature-interaction module between two branches is designed for strengthening the feature representation by allowing dynamic exchange of spatial and channel information, so as to retain more spatial details and inhibit irrelevant noise. Moreover, an auxiliary prediction task is introduced to learn the global geometric information, highlighting the boundary of the skin lesion. Comprehensive experiments using four publicly available skin lesion datasets (i.e., ISIC 2018, ISIC 2017, ISIC 2016, and PH2) indicated that CFF-Net outperformed the state-of-the-art models. In particular, CFF-Net greatly increased the average Jaccard Index score from 79.71% to 81.86% in ISIC 2018, from 78.03% to 80.21% in ISIC 2017, from 82.58% to 85.38% in ISIC 2016, and from 84.18% to 89.71% in PH2 compared with U-Net. Ablation studies demonstrated the effectiveness of each proposed component. Cross-validation experiments in ISIC 2018 and PH2 datasets verified the generalizability of CFF-Net under different skin lesion data distributions. Finally, comparison experiments using three public datasets demonstrated the superior performance of our model. The proposed CFF-Net performed well in four public skin lesion datasets, especially for challenging cases with blurred edges of skin lesions and low contrast between skin lesions and background. CFF-Net can be employed for other segmentation tasks with better prediction and more accurate delineation of boundaries. [ABSTRACT FROM AUTHOR]

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas. [ABSTRACT FROM AUTHOR]

Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices. [ABSTRACT FROM PUBLISHER]

People upload daily a huge number of portrait face pictures on websites and social media, which can be processed using biometric systems based on the face characteristics to perform an automatic recognition of the individuals. However, the performance of face recognition approaches can be limited by negative factors as aging, occlusions, rotations, and uncontrolled expressions. Nevertheless, the constantly increasing quality and resolution of the portrait pictures uploaded on websites and social media could permit to overcome these problems and improve the robustness of biometric recognition methods by enabling the analysis of additional traits, like the iris. To point the attention of the research community to the possible use of iris-based recognition techniques for images uploaded on websites and social media, we present a public image dataset called I-SOCIAL-DB (Iris Social Database). This dataset is composed of 3,286 ocular regions, extracted from 1,643 high-resolution face images of 400 individuals, collected from public websites. For each ocular region, a human expert extracted the coordinates of the circles approximating the inner and outer iris boundaries and performed a pixelwise segmentation of the iris contours, occlusions, and reflections. This dataset is the first collection of ocular images from public websites and social media, and one of the biggest collections of manually segmented ocular images in the literature. In this paper, we also present a qualitative analysis of the samples, a set of testing protocols and figures of merit, and benchmark results achieved using publicly available iris segmentation and recognition algorithms. We hope that this initiative can give a new test tool to the biometric research community, aiming to stimulate new studies in this challenging research field. Unlabelled Image • I-SOCIAL-DB is a novel public labeled dataset of ocular images. • I-SOCIAL-DB includes iris segmentation pixel-wise masks created by a human expert. • This paper offers an analysis of the samples based on statistical figures of merit. • This paper presents benchmark results achieved by using public software libraries. • I-SOCIAL-DB represents a new tool for the biometric research community. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR), most algorithms of which have employed and relied on sufficient training samples to receive a strong discriminative classification model, has remained a challenging task in recent years, among which the challenge of SAR data acquisition and further insight into the intuitive features of SAR images are the main concerns. In this paper, a deep transferred multi-level feature fusion attention network with dual optimized loss, called a multi-level feature attention Synthetic Aperture Radar network (MFFA-SARNET), is proposed to settle the problem of small samples in SAR ATR tasks. Firstly, a multi-level feature attention (MFFA) network is established to learn more discriminative features from SAR images with a fusion method, followed by alleviating the impact of background features on images with the following attention module that focuses more on the target features. Secondly, a novel dual optimized loss is incorporated to further optimize the classification network, which enhances the robust and discriminative learning power of features. Thirdly, transfer learning is utilized to validate the variances and small-sample classification tasks. Extensive experiments conducted on a public database with three different configurations consistently demonstrate the effectiveness of our proposed network, and the significant improvements yielded to surpass those of the state-of-the-art methods under small-sample conditions. [ABSTRACT FROM AUTHOR]

Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model's recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets. [ABSTRACT FROM AUTHOR]