State of the Art in Biometric Key Binding and Key Generation Schemes

Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems

A Multi-Biometric System Based on Feature and Score Level Fusions

In general, the information of multiple biometric modalities is fused at a single level, for example, score level or feature level. The recognition accuracy of a multimodal biometric system may not be improved by carrying fusion at a single level, since one matcher may provide a performance lower than that provided by other matchers. In view of this, we propose a new fusion scheme, referred to as the matcher performance-based (MPb) fusion scheme, in which the fusion is carried out at two levels, feature level, and score level, to improve the overall recognition accuracy. First, we consider the performance of the individual matchers in order to find out which of the modalities should be used for fusion at the feature level. Then, the selected modalities are fused at this level by utilizing their encoded features. Next, we fuse the score obtained from the feature-level fusion with that of the modality for which the performance is the highest. In order to carry out this fusion, a new normalization technique referred to as the overlap extrema-variation-based anchored min-max (OEVBAMM) normalization technique, is also proposed. By considering three modalities, namely, fingerprint, palmprint, and earprint, the performance of the proposed fusion scheme as well as that of the single level fusion scheme, both with various normalization and weighting techniques are evaluated in terms of a number of metrics. It is shown that the multi-biometric system based on the proposed fusion scheme provides the best performance when it employs the new normalization technique and the confidence-based weighting (CBW) method

Performance analysis of multimodal biometric fusion

Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.State of Kuwait – The Public Authority of Applied Education and Trainin

Unimodal and multimodal biometric sensing systems : a review

Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639am2017Electrical, Electronic and Computer Engineerin

A Multimodal and Multi-Algorithmic Architecture for Data Fusion in Biometric Systems

Software di autenticazione basato su tratti biometric

Palmprint Gender Classification Using Deep Learning Methods

Gender identification is an important technique that can improve the performance of authentication systems by reducing searching space and speeding up the matching process. Several biometric traits have been used to ascertain human gender. Among them, the human palmprint possesses several discriminating features such as principal-lines, wrinkles, ridges, and minutiae features and that offer cues for gender identification. The goal of this work is to develop novel deep-learning techniques to determine gender from palmprint images. PolyU and CASIA palmprint databases with 90,000 and 5502 images respectively were used for training and testing purposes in this research. After ROI extraction and data augmentation were performed, various convolutional and deep learning-based classification approaches were empirically designed, optimized, and tested. Results of gender classification as high as 94.87% were achieved on the PolyU palmprint database and 90.70% accuracy on the CASIA palmprint database. Optimal performance was achieved by combining two different pre-trained and fine-tuned deep CNNs (VGGNet and DenseNet) through score level average fusion. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was also implemented to ascertain which specific regions of the palmprint are most discriminative for gender classification