Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.

Finger-vein biometrics has been extensively investigated for personal authentication. One of the open issues in finger-vein verification is the lack of robustness against image-quality degradation. Spurious and missing features in poor-quality images may degrade the system's performance. Despite recent advances in finger-vein quality assessment, current solutions depend on domain knowledge. In this paper, we propose a deep neural network (DNN) for representation learning to predict image quality using very limited knowledge. Driven by the primary target of biometric quality assessment, i.e., verification error minimization, we assume that low-quality images are falsely rejected in a verification system. Based on this assumption, the low- and high-quality images are labeled automatically. We then train a DNN on the resulting data set to predict the image quality. To further improve the DNN's robustness, the finger-vein image is divided into various patches, on which a patch-based DNN is trained. The deepest layers associated with the patches form together a complementary and an over-complete representation. Subsequently, the quality of each patch from a testing image is estimated and the quality scores from the image patches are conjointly input to probabilistic support vector machines (P-SVM) to boost quality-assessment performance. To the best of our knowledge, this is the first proposed work of deep learning-based quality assessment, not only for finger-vein biometrics, but also for other biometrics in general. The experimental results on two public finger-vein databases show that the proposed scheme accurately identifies high- and low-quality images and significantly outperforms existing approaches in terms of the impact on equal error-rate decrease.

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we propose a single-sample-per-person (SSPP) palm-vein identification approach, where only a single sample per class is enrolled in the gallery set for training. Our approach, named MSMDGAN + CNN, consists of a multi-scale and multi-direction generative adversarial network (MSMDGAN) for data augmentation and a convolutional neural network (CNN) for palm-vein identification. First, a novel data augmentation approach, MSMDGAN, is developed to learn the internal distribution of patches in a single image. The proposed MSMDGAN consists of multiple fully convolutional GANs, each of which is responsible for learning the patch distribution within an image at a different scale and at a different direction. Second, given the resulting augmented data by MSMDGAN, we design a CNN for single sample palm-vein recognition. The experimental results on two public hand-vein databases demonstrate that MSMDGAN is able to generate realistic and diverse samples, which, in turn, improves the stability of the CNN. In terms of accuracy, MSMDGAN + CNN outperforms other representative approaches and achieves state-of-the-art recognition results.

This paper presents a new scheme to improve the performance of finger-vein identification systems. Firstly, a vein pattern extraction method to extract the finger-vein shape and orientation features is proposed. Secondly, to accommodate the potential local and global variations at the same time, a region-based matching scheme is investigated by employing the Scale Invariant Feature Transform (SIFT) matching method. Finally, the finger-vein shape, orientation and SIFT features are combined to further enhance the performance. The experimental results on databases of 426 and 170 fingers demonstrate the consistent superiority of the proposed approach.

Vein biometrics is a high security and privacy preserving identification technology that has attracted increasing attention over the last decade. Deep neural networks (DNNs), such as convolutional neural networks (CNN), have shown strong capabilities for robust feature representation, and have achieved, as a result, state-of-the-art performance on various vision tasks. Inspired by their success, deep learning models have been widely investigated for vein recognition and have shown significant improvement of identification accuracy compared to handcrafted models. Existing deep learning models, however, are vulnerable to adversarial perturbation attacks, where thoughtfully crafted small perturbations can cause misclassification of legitimate images, degrading, thereby, the efficiency of vein recognition systems. To address this problem, we propose, in this paper, VeinGuard, a novel defense framework to defend deep learning classifiers against adversarial palm-vein image attacks, composed of a local transformer-based GAN and a purifier. VeinGuard comprises two components: a local transformer-based GAN (LTGAN) that learns the distribution of unperturbed vein images and generates high-quality palm-vein images, and a purifier consisting of a trainable residual network and of a pre-trained generator from LTGAN that automatically removes a wide variety of adversarial perturbations. The resulting clean images are fed to vein classifiers for identification, thereby avoiding adversarial attacks. We evaluate VeinGuard on three public vein datasets in terms of white-box attacks, black-box attacks, ablation experiments, and computation time. The experimental results show that VeinGuard allows filtering the perturbations and enables the classifiers to achieve state-of-the-art recognition results for different adversarial attacks.

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially-designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, OC-SVM classifier, and security analysis. The experimental results show that ADFFDA obtains the best authentication performance w.r.t representative approaches, by achieving a mean equal error rate of 0.01%.