Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

Demographic Bias: A Challenge for Fingervein Recognition Systems?

Recently, concerns regarding potential biases in the underlying algorithms of many automated systems (including biometrics) have been raised. In this context, a biased algorithm produces statistically different outcomes for different groups of individuals based on certain (often protected by anti-discrimination legislation) attributes such as sex and age. While several preliminary studies investigating this matter for facial recognition algorithms do exist, said topic has not yet been addressed for vascular biometric characteristics. Accordingly, in this paper, several popular types of recognition algorithms are benchmarked to ascertain the matter for fingervein recognition. The experimental evaluation suggests lack of bias for the tested algorithms, although future works with larger datasets are needed to validate and confirm those preliminary results.Comment: 5 pages, 2 figures, 8 tables. Submitted to European Signal Processing Conference (EUSIPCO) -- special session on bias in biometric

Patterns Identification of Finger Outer Knuckles by Utilizing Local Directional Number

Finger Outer Knuckle (FOK) is a distinctive biometric that has grown in popularity recently. This results from its inborn qualities such as stability, protection, and specific anatomical patterns. Applications for the identification of FOK patterns include forensic investigations, access control systems, and personal identity. In this study, we suggest a method for identifying FOK patterns using Local Directional Number (LDN) codes produced from gradient-based compass masks. For the FOK pattern matching, the suggested method uses two asymmetric masks—Kirsch and Gaussian derivative—to compute the edge response and extract LDN codes. To calculate edge response on the pattern, an asymmetric compass mask made from the Gaussian derivative mask is created by rotating the Kirsch mask by 45 degrees to provide edge response in eight distinct directions. The edge response of each mask and the combination of dominating vector numbers are examined during the LDN code-generating process. A distance metric can be used to compare the LDN code\u27s condensed representation of the FOK pattern to the original for matching purposes. On the Indian Institute of Technology Delhi Finger Knuckle (IITDFK) database, the efficiency of the suggested procedure is assessed. The data show that the suggested strategy is effective, with an Equal Error Rate (EER) of 10.78%. This value performs better than other EER values when compared to different approaches

Convolutional neural networks for face recognition and finger-vein biometric identification

The Convolutional Neural Network (CNN), a variant of the Multilayer Perceptron (MLP), has shown promise in solving complex recognition problems, particularly in visual pattern recognition. However, the classical LeNet-5 CNN model, which most solutions are based on, is highly compute-intensive. This CNN also suffers from long training time, due to the large number of layers that ranges from six to eight. In this research, a CNN model with a reduced complexity is proposed for application in face recognition and finger-vein biometric identification. A simpler architecture is obtained by fusing convolutional and subsampling layers into one layer, in conjunction with a partial connection scheme applied between the first two layers in the network. As a result, the total number of layers is reduced to four. The number of feature maps at each layer is optimized according to the type of image database being processed. Consequently, the numbers of network parameters (including neurons, trainable parameters and connections) are significantly reduced, essentially increasing the generalization ability of the network. The Stochastic Diagonal Levenberg-Marquadt (SDLM) backpropagation algorithm is modified and applied in the training of the proposed network. With this learning algorithm, the convergence rate is accelerated such that the proposed CNN converges within 15 epochs. For face recognition, the proposed CNN achieves recognition rates of 100.00% and 99.50% for AT&T and AR Purdue face databases respectively. Recognition time on the AT&T database is less than 0.003 seconds. These results outperform previous existing works. In addition, when compared with the other CNN-based face recognizer, the proposed CNN model has the least number of network parameters, hence better generalization ability. A training scheme is also proposed to recognize new categories without full CNN training. In this research, a novel CNN solution for the finger-vein biometric identification problem is also proposed. To the best of knowledge, there is no previous work reported in literature that applied CNN for finger-vein recognition. The proposed method is efficient in that simple preprocessing algorithms are deployed. The CNN design is adapted on a finger-vein database, which is developed in-house and contains 81 subjects. A recognition accuracy of 99.38% is achieved, which is similar to the results of state-of-the-art work. In conclusion, the success of the research in solving face recognition and finger-vein biometric identification problems proves the feasibility of the proposed CNN model in any pattern recognition system

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers