New Finger Biometric Method Using Near Infrared Imaging

In this paper, we propose a new finger biometric method. Infrared finger images are first captured, and then feature extraction is performed using a modified Gaussian high-pass filter through binarization, local binary pattern (LBP), and local derivative pattern (LDP) methods. Infrared finger images include the multimodal features of finger veins and finger geometries. Instead of extracting each feature using different methods, the modified Gaussian high-pass filter is fully convolved. Therefore, the extracted binary patterns of finger images include the multimodal features of veins and finger geometries. Experimental results show that the proposed method has an error rate of 0.13%

Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbor Classifier

The k-nearest centroid neighbor kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighborhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbor. In this work, an adaptive k-nearest centroid neighbor (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighborhood size of the test sample. The neighborhood size for the test sample is changed through the following ways: 1) The neighborhood size, k will be adapted to j if the centroid distance of j-th nearest centroid neighbor is greater than the predefined boundary. 2) There is no need to look for further nearest centroid neighbors if the maximum number of samples of the same class is found among jth nearest centroid neighbor. Thus, the size of neighborhood is adaptively changed to j. Experimental results on theFinger Vein USM (FV-USM) image database demonstrate the promising results in which the classification time of the akNCN classifier is significantly reduced to 51.56% in comparison to the closest competitors, kNCN and limited-kNCN. It also outperforms its competitors by achieving the best reduction ratio of 12.92% whilemaintaining the classification accuracy

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

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

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