Human Identification Using Finger Vein and Texture Matching

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Multispectral Palmprint Encoding and Recognition

Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003% on PolyU and 0.2% on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

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

Liveness Detection on Fingers Using Vein Pattern

Tato práce se zabývá rozšířením snímače otisků prstů Touchless Biometric Systems 3D-Enroll o jednotku detekce živosti prstu na základě žil. Bylo navrhnuto a zkonstruováno hardwarové řešení s využitím infračervených diod. Navržené softwarové řešení pracuje ve dvou různých režimech: detekce živosti na základě texturních příznaků a verifikace uživatelů na základě porovnávání žilních vzorů. Datový soubor obsahující přes 1100 snímků jak živých prstů tak jejich falsifikátů vznikl jako součást této práce a výkonnost obou zmíněných režimů byla vyhodnocena na tomto datovém souboru. Na závěr byly navrhnuty materiály vhodné k výrobě falsifikátů otisků prstů umožňující oklamání detekce živosti pomocí žilních vzorů.This work presents liveness detection extension of the Touchless Biometric Systems 3D-Enroll fingerprint sensor which is based on finger vein pattern. Hardware solution was designed and realized using infrared diodes. Designed software system operates in two different modes: liveness detection based on texture features and user verification using finger vein matching. A dataset containing more than 1,100 images of both real fingers and their falsifications was gathered. Performance of both proposed modes was evaluated using mentioned dataset and suitable materials, that can fool the liveness detection module, were highlighted.

Multimodal biometric authentication algorithm at score level fusion using hybrid optimization

Biometric is emerging technology in identification and authentication of human being with more reliable and accurate. Combining multiple biometric systems is a promising solution to provide more security. It eliminates the disadvantages of unimodal biometric systems such as non-universality, noise in sensed data, intra-class variations, distinctiveness, spoof attacks and traditional method of authenticating a human and their identity. The proposed method depicts a multimodal biometric algorithm is designed to recognize individuals for robust and secured authentication using normalized score level fusion techniques with hybrid Genetic Algorithm and Particle Swarm Optimization for optimization in order to reduce False Acceptance Rate and False Rejection Rate and to enhance Equal Error Rate and Accuracy.

Improved methods for finger vein identification using composite median-wiener filter and hierarchical centroid features extraction

Finger vein identification is a potential new area in biometric systems. Finger vein patterns contain highly discriminative characteristics, which are difficult to be forged because they reside underneath the skin of the finger and require a specific device to capture them. Research have been carried out in this field but there is still an unresolved issue related to low-quality data due to data capturing and processing. Low-quality data have caused errors in the feature extraction process and reduced identification performance rate in finger vein identification. To address this issue, a new image enhancement and feature extraction methods were developed to improve finger vein identification. The image enhancement, Composite Median-Wiener (CMW) filter would improve image quality and preserve the edges of the finger vein image. Next, the feature extraction method, Hierarchical Centroid Feature Method (HCM) was fused with statistical pixel-based distribution feature method at the feature-level fusion to improve the performance of finger vein identification. These methods were evaluated on public SDUMLA-HMT and FV-USM finger vein databases. Each database was divided into training and testing sets. The average result of the experiments conducted was taken to ensure the accuracy of the measurements. The k-Nearest Neighbor classifier with city block distance to match the features was implemented. Both these methods produced accuracy as high as 97.64% for identification rate and 1.11% of equal error rate (EER) for measures verification rate. These showed that the accuracy of the proposed finger vein identification method is higher than the one reported in the literature. As a conclusion, the results have proven that the CMW filter and HCM have significantly improved the accuracy of finger vein identification

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

Generating and analyzing synthetic finger vein images

Abstract: The finger-vein biometric offers higher degree of security, personal privacy and strong anti-spoofing capabilities than most other biometric modalities employed today. Emerging privacy concerns with the database acquisition and lack of availability of large scale finger-vein database have posed challenges in exploring this technology for large scale applications. This paper details the first such attempt to synthesize finger-vein images and presents analysis of synthesized images for the biometrics authentication. We generate a database of 50,000 finger vein images, corresponding to 5000 different subjects, with 10 different synthesized finger-vein images from each of the subject. We use tractable probability models to compare synthesized finger-vein images with the real finger- vein images for their image variability. This paper also presents matching accuracy using the synthesized finger-vein database from 5000 different subjects, using 225000 genuine and 1249750000 impostor matching scores, which suggests significant promises from this finger-vein biometric modality for large scale biometrics applications