Search

Knee disorders are common among the human population. Knee osteoarthritis (OA) is the most widespread knee joint disorder, which may require surgical treatment. The detection and diagnosis of knee joint disorders from medical images demand enormous human effort and time. The development of a computer‐aided diagnosis (CAD) system can notably minimise the burden of medical experts and remove the intra‐observer and inter‐observer variations. To achieve the goal, the highly challenging research problem of knee image segmentation has been frequently paid attention in past years, which can be efficiently applied in the development of the CAD system. Knee image segmentation is a challenging task owing to the image contrasts, intensity variations, shape irregularities, and the presence of thin cartilage structures. Therefore, this paper presents a literature review of automated segmentation approaches mainly focused on the segmentation of knee cartilage and bone, with respect to the underlying technical aspects, datasets used, and the performance reported. The paper also presents the growth from classical segmentation approaches towards the deep learning approaches in the knee image segmentation. Owing to the varying quality and complexity of different knee image datasets, this paper abstains from doing a rigorous comparative evaluation of image segmentation approaches.

Latent fingerprint is considered as the key evidence during crime scene investigations. Various powder and chemical methods are available for visualizing the latent fingerprints as these finger impressions are not directly visible through the naked eye. However, these methods may damage the finger impressions in case if they are not properly lifted and handled carefully. Preserving the evidential value of the located latent fingerprints, hence becomes pivotal for analyzing and identifying the suspected individual. Nowadays, optical touchless technology is being prevalent for developing and visualizing the latent finger impressions. Reflected Ultra Violet Imaging System (RUVIS) is one such optical touchless device. There are number of powder based latent fingerprint databases available. However, database of latent fingerprints using optical touchless technology is not available in the literature. The paper presents the latent fingerprint database developed and captured using the touch-less acquisition device (RUVIS). Extraction of level 3 features from the latent fingerprints plays a significant role in matching these latent impressions with plain impressions. Further in this paper, level 3 features, particularly pores are extracted using Fully Convolution Neural Network (FCN) from the collected latent fingerprints using the RUVIS.

Biometric systems are used for the unique identification of a person by using one or more biological traits. There are a number of attack points (levels) in biometric systems, but the major concern is about the security at template level. It is salient that the biometric system should be strong enough to resist the attacks and template theft by the intruders. To overcome the security issues related to the stored template, a bio-cryptosystem primitive technique called fuzzy extractor (FE) is extensively used in variety of biometric applications. FE is a key generation process which extracts the random key from a piece of live biometric data in an error-tolerant way. Thus, for the enhancement of the template security FE acts as a better technique than other protection schemes. This paper firstly explains the basic concept of FE and then presents the review of the FE based on the different traits and combinational approaches. The paper also compares the FE with other protection schemes based on the merits and demerits. The paper concludes the selection of the better biometric combination for practical implementation.

Currently, biometrics is being widely used for authentication and identification of an individual. The biometric systems itself needs to be more secured and reliable so it they can provide secure authentication in various applications. To optimize the security, it is vital that biometric authentication frameworks are intended to withstand various sources of attack. In security sensitive applications, there is a shrewd adversary component which intends to deceive the detection system. In a well-motivated attack scenario, in which there exists an attacker who may try to evade a well-established system at test time by cautiously altering attack samples, i.e., Evasion Attack. The aim of this work is to demonstrate that machine learning can be utilized to enhance system security, if one utilizes an adversary-aware approach that proactively intercept the attacker. Also, we present a basic but credible gradient based approach of evasion attack that can be exploited to methodically acquire the security of a Fingerprint Biometric Database.

Authentication using biometrics has an edge over traditional authentication mechanisms. Fuzzy vault is an eminent biometric cryptosystem technique that aims to protect a secret using a biometric template. Authorized users can, however, access the secret by providing the valid biometric. In a fuzzy vault, noise is incorporated in the form of chaff points in order to increase the security. While implementing fuzzy vault schemes in real scenarios, the most critical but computing-intensive and time-consuming task is chaff generation. In this paper, a neoteric method for chaff generation is proposed, which is less time-consuming in generating large numbers of chaff points.

Latent fingerprints are considered as one of the important evidences obtained from the site of crime. The process of developing, acquiring, processing and matching of latent fingerprints is different from the inked or live-scan fingerprints. Automated identification of latent fingerprints is still in its nascent phase when compared with the Automatic Fingerprint Identification System (AFIS) used by the police department. This paper provides an extensive review of the work done by eminent researchers in the development of an automated latent fingerprint identification system.

Biometric technology is rapidly being adopted in wide variety of security applications. However, the system itself is not completely foolproof and is vulnerable to many attacks. Some of the attacks on the biometric system are very severe, one of which is the attack on template security. In spite of the various template security techniques presented in the literature, none of them is able to provide security, diversity, revocability, and good performance simultaneously to the biometric system. Fuzzy vault is one of the most promising bio-cryptographic techniques to prevent the template data from being misused. To make the fuzzy vault practically realizable in real-life applications especially for large databases, the chaff generation time needs to be reduced to a greater extent. This work focuses on decreasing the chaff generation time to reduce the overall vault creation time. The approach presented in the paper has also been tested on real-time dedicated hardware using fingerprint data acquired in real time by using a fingerprint sensor Verifier 300LC to bridge the gap between the research and real-time application scenarios.

Biometric Systems are the personal identification systems that use behavioral and physiological characteristics of a person. One of the main concerns in biometrics systems is template security. Fuzzy vault, a bio-cryptosystem, is used to provide security to the stored templates. Fuzzy vault has proven to be a very good security technique, nonetheless it lacks in providing revocability and security against correlation attacks. Thus for the enhancement of the security of fuzzy vault and to overcome the limitation of correlation attack, techniques like hybrid model and multimodal biometrics can be used. This paper gives a review of the above mentioned techniques, viz. hybrid and multimodal, and how they can be effective in enhancing the security of the system.

The encryption techniques, biometrics and cryptography integrate to form biometric cryptosystems. These are designed either to bind a cryptographic key or to generate cryptographic key using biometric features. The deployment of bio-cryptosystem technique, namely fuzzy extractor in multimodal biometric system leads to increase in user privacy and system security. This paper provides with a framework where feature level fusion of iris and dual fingerprint forms a multimodal template and key is generated using fuzzy extractors, in order to provide reliability and good recognition performance. The hash function is used to protect the key generated from biometric traits. Since fuzzy extractor operates only on ordered dataset. However the minutiae points of fingerprint are unordered, so an algorithm is designed for conversion of unordered minutiae points to ordered minutiae dataset to make it consistent for key generation methods.

Biocryptosystem is an emerging concept in the field of template security because it has advantages of cryptography and biometrics. Rather than randomly generating a cryptography key, fuzzy extractor is used to extract a cryptography key from biometric template itself. This paper proposes a multitrait fingervein and iris robust key extractor biocryptosystem which provides authentication and security of biometric data. The proposed scheme predicts using message authentication code with key manipulation security function whether any changes have been made in the publically stored secure sketch by an adversary and thereby make the system more robust. A common reference string model is used by random extractor function of fuzzy extractor to extract the secure cryptography key. The performance of the proposed system is measured using false acceptance rate and false rejection rate as the performance measures. The experimental results are accomplished using two different dataset CASIAv4 and live data of iris and one dataset of finger vein.

Biometric security systems are gaining strength these days as compared to conventional authentication methods. But with the advancements in biometric systems, the attempts of attacks on these systems are also increasing day by day. Even though it is difficult for an intruder to steal the biometric trait of a person, he can still breach into the security of a biometric system. Biometric System can be implemented as unimodal and multimodal systems depending upon the application requirements. Multimodal biometrics systems however are more reliable and stable as compared to unimodal systems. The choice of fusion strategy while designing a multimodal biometric system has a direct impact on its performance. Out of the various fusion strategies, feature level fusion is considered more robust. The extracted feature set from the multiple biometric traits used for multimodal system design may be incompatible. Incompatible biometric feature sets have to be made compatible by using suitable conversion. The paper proposes a Multimodal Biometric systems using face and fingerprint traits with Fuzzy Vault template security.

Biometrie based authentication system authenticates an individual on the basis of biometrie template stored during enrolment. As the biometric features are immutable, therefore a security concern about the stored biometric template arises. Fingerprint fuzzy vault is a cryptographic construct securing the biometric template and allowing error tolerant fingerprint based authentication. In this paper, secure fingerprint fuzzy vault is proposed in which fingerprint minutiae points are transformed using Fast Walsh Hadamard Transformation. This paper also proposes a new chaff point generation method which is faster than existing methods in generating large number of chaff points. Performance of the proposed methodology is estimated by calculating False Acceptance Rate and False Rejection Rate.

system that based on single biometric called uni- modal biometrics usually suffers from problems like imposter's attack or hacking, unacceptable error rate and low performance. So the need of using multimodal biometric system arises in such cases. The aim of this paper is to study the fusion at feature extraction level for face and fingerprint. The proposed system fuses the two traits at feature extraction level by first making the feature sets compatible for concatenation and then reducing the feature sets to handle the "problem of curse of dimensionality". After concatenation these features are classified. Features of both modalities are extracted using Gabor filter and Principal Component Analysis (PCA). K-Nearest Neighbour classifier is used to classify the different people in the database. The experimental results reveal that the fusion of more than one biometric trait at feature level fusion with the K-Nearest Neighbor technique exhibits robust performance and increases its performance with utmost level of accuracy.

Driver fatigue is one of the most common reasons for deadly road accidents around the world. Continuous monotonous driving for long hours without rest causes drowsiness and consequently fatal road accidents. Automatic driver drowsiness detection can prevent a vast number of sleep persuaded road accidents, and hence can save precious lives. Number of techniques for driver drowsiness detection has been examined in the recent past. This paper presents a survey of these techniques. These techniques detect the driver drowsiness by observing the driving pattern. Abnormalities in driving pattern is hypothesized as a drowsiness state of driver. Various measures such as subjective, behavioral, physiological, and vehicular have been used for this purpose. The comparative analysis of these techniques indicates that behavioral measures are easy to acquire and does not disturb the driver as they are non-invasive. Among various behavioral measures, head movement measure is found to be most precise and effective.

In this paper, a review of soft computing techniques in biometrics is presented. Biometrics has become one of the most promising authentication techniques in the last few years but issues like False Acceptance Rate, False Rejection Rate still prevails in biometrics. An efficient biometric system has higher recognition rate, tolerance for imprecision, uncertainty and noisy data. Recently, Soft Computing has gained wide popularity in biometric recognition where it has helped in improving the recognition rate to a great extent. Various soft computing techniques like fuzzy logic, evolutionary algorithm and artificial neural network has increasingly being used for the construction of efficient biometric systems. This paper first presents the introduction to biometrics along with the issues involved in it. A brief description of various soft computing techniques for feature extraction, fusion, feature optimization, improvement of recognition rate in biometrics is provided. Finally future research areas are presented.

This paper discusses about Enhanced iris recognition which is used to overcome some of the problem like to automate the recognition of the iris by reducing complexity and increasing algorithm speed. Various challenges are faced while working with the iris recognition system. Iris recognition systems make use of the uniqueness of the iris patterns to derive a unique mapping. Iris recognition, as a biometric method, outperforms others because of its high accuracy. Iris recognition also has the ability to handle very large populations at high speed. Mostly three stages are followed while working with iris system i.e. preprocessing, feature extraction and recognition stage. This paper presents an automated and novel iris recognition system where overall computational match speed is reduced (from iris preprocessing to the final stage of recognition) and hence makes system more reliable with accuracy of 99.38% and low FAR.

Security is a very important aspect in the biometric system. There are number attacks and there remedial solutions discussed in the literature on different modules of biometrics system and communication links among them. But still the researchers are not able to secure every module of a biometric system against these attacks. Template and database are the very important parts of biometric systems and attacker mostly attack on template and database of biometric system so securing them is a very crucial issue these days. In this research paper our focus is on template and data base security in biometrics system and we develop a system to encrypt and decrypt the biometric image using helper data of a fingerprint and password to make it secure so that even if someone gains access to the encrypted image stored in the database he will not able to reproduce the original image from it and it will be useless for him.

Security of cryptographic keys is an important issue in today's unprotected world. Most reliable solution of this problem is state of art fuzzy vault which is an application of bio-cryptosystems. These systems use biometric trait for locking and unlocking the keys. During unlocking, it is necessary to do alignment of query biometric data with enrolled biometric data because of large variation in two samples of a biometric trait of a user taken over the short span of time. Complexity of alignment is increased due to absence of original fingerprint template during alignment. That's why helper data or additional information is required for alignment. There are number of techniques developed for automatic alignment of fingerprints till now. This paper gives the review of most of those methods.

Most fingerprint recognition techniques are based on minutiae matching and have been well studied. However, this technology still suffers from problems associated with the handling of poor quality impressions. One problem besetting fingerprint matching is distortion. Distortion changes both geometric position and orientation, and leads to difficulties in establishing a match among multiple impressions acquired from the same finger tip. Marking all the minutiae accurately as well as rejecting false minutiae is another issue still under research. Our work has combined many methods to build a minutia extractor and a minutia matcher. The combination of multiple methods comes from a wide investigation into research papers. Also some novel changes like segmentation using Morphological operations, improved thinning, false minutiae removal methods, minutia marking with special considering the triple branch counting, minutia unification by decomposing a branch into three terminations, and matching in the unified x-y coordinate system after a two-step transformation are used in the work., {"references":["A. Jain, R. Bolle, and S. Pankanti, \"Biometrics Personal Identification in\nNetworked Society\", Kluwer Academic Publishers New York, Boston,\nDordrecht, London, Moscow, pp. 1-64, 2002.","A. K. Jain, F. Patrick, A. Arun, \"Handbook of Biometrics\", Springer\nScience+Business Media, LLC, 1st edition, pp. 1-42, 2008.","D. Maio, and D. Maltoni, \"Direct gray-scale minutiae detection in\nfingerprints\", IEEE Transactions Pattern Analysis and Machine\nIntelligence, vol. 19(1), pp. 27-40, 1997.","D. Maltoni, D. Maio, and A. Jain, S. Prabhakar, \"4.3: Minutiae-based\nMethods- (extract) from Handbook of Fingerprint Recognition\",\nSpringer, New York, pp. 141-144, 2003.","E. Hastings, \"A Survey of Thinning Methodologies\", Pattern analysis\nand Machine Intelligence, IEEE Transactions, vol. 4, Issue 9, pp. 869-\n885, 1992.","L. Hong, \"Automatic Personal Identification Using Fingerprints\", Ph.D.\nThesis, 1998.","L. Lam, S. W. Lee, and C. Y. Suen, \"Thinning Methodologies-A\nComprehensive Survey\", IEEE Transactions on Pattern analysis and\nmachine intelligence, vol. 14, no. 9, 1992.","L.C. Jain, U. Halici, I. Hayashi, S.B. Lee, and S. Tsutsui, \"Intelligent\nbiometric techniques in fingerprint and face recognition\", The CRC\nPress, 1999.","K. Nallaperumall, A. L. Fred, and S. Padmapriya, \"A Novel Technique\nfor Fingerprint Feature Extraction Using Fixed Size Templates\", IEEE\n2005 Conference, pp. 371-374, 2005.\n[10] P. Komarinski, P. T. Higgins, and K. M. Higgins, K. Fox Lisa,\n\"Automated Fingerprint Identification Systems (AFIS)\", Elsevier\nAcademic Press, pp. 1-118, 2005."]}