Deep Representation-Based Feature Extraction and Recovering for Finger-Vein Verification
Huafeng Qin,Mounim A. El-Yacoubi +1 more
TLDR
A deep learning model is proposed to extract and recover vein features using limited a priori knowledge to recover missing finger-vein patterns in the segmented image.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
Understanding Deep Learning Techniques for Image Segmentation
TL;DR: The main goal of this work is to provide an intuitive understanding of the major techniques that have made a significant contribution to the image segmentation domain.
Journal ArticleDOI
Convolutional Neural Network for Finger-Vein-Based Biometric Identification
TL;DR: A convolutional-neural-network-based finger-vein identification system is proposed and the accuracy achievable with the proposed approach can go beyond 95% correct identification rate for all the four considered publicly available databases.
Journal ArticleDOI
A novel finger vein verification system based on two-stream convolutional network learning
TL;DR: A lightweight two-channel network that has only three convolution layers for finger vein verification and a two-stream network to integrate the original image and the mini-ROI that achieves results superior to the current state of the art on both the MMCBNU and SDUMLA databases.
Journal ArticleDOI
A Systematic Review of Finger Vein Recognition Techniques
TL;DR: The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark, and some novel findings are listed after the critical comparative analysis of the highlighted techniques.
Journal ArticleDOI
FV-GAN: Finger Vein Representation Using Generative Adversarial Networks
TL;DR: A novel approach termed FV-GAN to finger vein extraction and verification, based on generative adversarial network (GAN), as the first attempt in this area, which adopts fully convolutional networks as the basic architecture and discards fully connected layers, which relaxes the constraint on the input image size and reduces the computational expenditure for feature extraction.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Book ChapterDOI
Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
Proceedings ArticleDOI
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
TL;DR: This work revisits both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network.
Proceedings ArticleDOI
Face recognition using eigenfaces
Matthew Turk,Alex Pentland +1 more
TL;DR: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described.