Finger Vein Recognition Based on a Personalized Best Bit Map

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition

PVSNet: Palm Vein Authentication Siamese Network Trained using Triplet Loss and Adaptive Hard Mining by Learning Enforced Domain Specific Features

Designing an end-to-end deep learning network to match the biometric features with limited training samples is an extremely challenging task. To address this problem, we propose a new way to design an end-to-end deep CNN framework i.e., PVSNet that works in two major steps: first, an encoder-decoder network is used to learn generative domain-specific features followed by a Siamese network in which convolutional layers are pre-trained in an unsupervised fashion as an autoencoder. The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese matching network using an adaptive margin based hard negative mining has been suggested. The hyper-parameters associated with the training strategy, like the adaptive margin, have been tuned to make the learning more effective on biometric datasets. In extensive experimentation, the proposed network outperforms most of the existing deep learning solutions on three type of typical vein datasets which clearly demonstrates the effectiveness of our proposed method.Comment: Accepted in 5th IEEE International Conference on Identity, Security and Behavior Analysis (ISBA), 2019, Hyderabad, Indi

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

DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication

Finger vein authentication, recognized for its high security and specificity, has become a focal point in biometric research. Traditional methods predominantly concentrate on vein feature extraction for discriminative modeling, with a limited exploration of generative approaches. Suffering from verification failure, existing methods often fail to obtain authentic vein patterns by segmentation. To fill this gap, we introduce DiffVein, a unified diffusion model-based framework which simultaneously addresses vein segmentation and authentication tasks. DiffVein is composed of two dedicated branches: one for segmentation and the other for denoising. For better feature interaction between these two branches, we introduce two specialized modules to improve their collective performance. The first, a mask condition module, incorporates the semantic information of vein patterns from the segmentation branch into the denoising process. Additionally, we also propose a Semantic Difference Transformer (SD-Former), which employs Fourier-space self-attention and cross-attention modules to extract category embedding before feeding it to the segmentation task. In this way, our framework allows for a dynamic interplay between diffusion and segmentation embeddings, thus vein segmentation and authentication tasks can inform and enhance each other in the joint training. To further optimize our model, we introduce a Fourier-space Structural Similarity (FourierSIM) loss function, which is tailored to improve the denoising network's learning efficacy. Extensive experiments on the USM and THU-MVFV3V datasets substantiates DiffVein's superior performance, setting new benchmarks in both vein segmentation and authentication tasks

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

Finger Vein Recognition Based on (2D)2 PCA and Metric Learning

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. In this paper, (2D)2 PCA is applied to extract features of finger veins, based on which a new recognition method is proposed in conjunction with metric learning. It learns a KNN classifier for each individual, which is different from the traditional methods where a fixed threshold is employed for all individuals. Besides, the SMOTE technology is adopted to solve the class-imbalance problem. Our experiments show that the proposed method is effective by achieving a recognition rate of 99.17%

Cross-Database Evaluation With an Open Finger Vein Sensor

Finger vein recognition is a recent biometric application, which relies on the use of human finger vein patterns beneath the skin's surface. While several methods have been proposed in the literature, its applicability to uncontrolled scenarios has not yet been shown. To this purpose this paper first introduces the VERA database, a new challenging publicly available database of finger vein images. This corpus consists of 440 index finger images from 110 subjects collected with an open device in an uncontrolled way. Second, an evaluation of state-of-the-art finger vein recognition systems is performed, both on the controlled UTFVP database and on the new VERA database. This is achieved using a new open source and extensible framework, which allows fair and reproducible benchmarks. Experimental results show that challenging recording conditions such as misalignments of the fingers lead to an absolute degradation in equal error rate of 2.75% up to 24.10% on VERA when compared to the best performances on UTFVP

Finger vein identification based on maximum curvature directional feature extraction / Yuhanim Hani Yahaya, Siti Mariyam Shamsuddin and Wong Yee Leng

Finger vein identification has become an important area of study especially in the field of biometric identification and has further potential in the field of forensics. The finger vein pattern has highly discriminative features that exhibit universality, uniqueness and permanence characteristics. Finger vein identification requires living body identification, which means that only vein in living finger can be captured and used for identification. Acquiring useful features from finger vein in order to reflect the identity of an individual is the main issues for identification. This research aims at improving the scheme of finger vein identification take advantage of the proposed feature extraction, which is Maximum Curvature Directional Feature (MCDF). Experimental results based on two public databases, SDUMLA-HMT datasets and PKU datasets show high performance of the proposed scheme in comparison with state-of-the art methods. The proposed approach scored 0.001637 of equal error rate (EER) for SDUMLAHMT dataset and 0.00431 of equal error rate for PKU datase