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

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

Deep Supervised Hashing using Symmetric Relative Entropy

By virtue of their simplicity and efficiency, hashing algorithms have achieved significant success on large-scale approximate nearest neighbor search. Recently, many deep neural network based hashing methods have been proposed to improve the search accuracy by simultaneously learning both the feature representation and the binary hash functions. Most deep hashing methods depend on supervised semantic label information for preserving the distance or similarity between local structures, which unfortunately ignores the global distribution of the learned hash codes. We propose a novel deep supervised hashing method that aims to minimize the information loss generated during the embedding process. Specifically, the information loss is measured by the Jensen-Shannon divergence to ensure that compact hash codes have a similar distribution with those from the original images. Experimental results show that our method outperforms current state-of-the-art approaches on two benchmark datasets

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices

Privacy-Preserving Biometric Authentication

Biometric-based authentication provides a highly accurate means of authentication without requiring the user to memorize or possess anything. However, there are three disadvantages to the use of biometrics in authentication; any compromise is permanent as it is impossible to revoke biometrics; there are significant privacy concerns with the loss of biometric data; and humans possess only a limited number of biometrics, which limits how many services can use or reuse the same form of authentication. As such, enhancing biometric template security is of significant research interest. One of the methodologies is called cancellable biometric template which applies an irreversible transformation on the features of the biometric sample and performs the matching in the transformed domain. Yet, this is itself susceptible to specific classes of attacks, including hill-climb, pre-image, and attacks via records multiplicity. This work has several outcomes and contributions to the knowledge of privacy-preserving biometric authentication. The first of these is a taxonomy structuring the current state-of-the-art and provisions for future research. The next of these is a multi-filter framework for developing a robust and secure cancellable biometric template, designed specifically for fingerprint biometrics. This framework is comprised of two modules, each of which is a separate cancellable fingerprint template that has its own matching and measures. The matching for this is based on multiple thresholds. Importantly, these methods show strong resistance to the above-mentioned attacks. Another of these outcomes is a method that achieves a stable performance and can be used to be embedded into a Zero-Knowledge-Proof protocol. In this novel method, a new strategy was proposed to improve the recognition error rates which is privacy-preserving in the untrusted environment. The results show promising performance when evaluated on current datasets

Contactless Palmprint Recognition System: A Survey

Information systems in organizations traditionally require users to remember their secret pins or (passwords), token, card number, or both to con�rm their identities. However, the technological trend has been moving towards personal identi�cation based on individual behavioural attributes (such as gaits, signature, and voice) or physiological attributes (such as palmprint, �ngerprint, face, iris, or ear). These attributes (biometrics) offer many advantages over knowledge and possession-based approaches. For example, palmprint images have rich, unique features for reliable human identi�cation, and it has received signi�cant attention due to their stability, reliability, uniqueness, and non-intrusiveness. This paper provides an overview and evaluation of contactless palmprint recognition system, the state-of-the-art performance of existing studies, different types of ``Region of Interest'' (ROI) extraction algorithms, feature extraction, and matching algorithms. Finally, the �ndings obtained are presented and discussed