Abstract
Among the members of biometric identifiers, the palmprint and the palmvein have received significant attention due to their stability, uniqueness, and non-intrusiveness. In this paper, we investigate the problem of palmprint/palmvein recognition and propose a Deep Convolutional Neural Network (DCNN) based scheme, namely P a l m R CNN (short for palmprint/palmvein recognition using CNNs). The effectiveness and efficiency of P a l m R CNN have been verified through extensive experiments conducted on benchmark datasets. In addition, though substantial effort has been devoted to palmvein recognition, it is still quite difficult for the researchers to know the potential discriminating capability of the contactless palmvein. One of the root reasons is that a large-scale and publicly available dataset comprising high-quality, contactless palmvein images is still lacking. To this end, a user-friendly acquisition device for collecting high quality contactless palmvein images is at first designed and developed in this work. Then, a large-scale palmvein image dataset is established, comprising 12,000 images acquired from 600 different palms in two separate collection sessions. The collected dataset now is publicly available.
Highlights
Personal authentication has become a vital and highly demanding technique which is the foundation of many applications, such as security access systems, time attendance systems, and forensics science [1]
Their results are unsatisfactory and have much room to improve. Different from these hand-crafted methods, deep convolutional neural networks (DCNN) can learn higher-level features from massive training samples via a deep architecture, and it can capture the representation for the task-specific knowledge. Considering this point, we propose a Deep Convolutional Neural Network (DCNN)-based scheme for the problem of palmprint/palmvein recognition, namely PalmRCNN, which can further extract the deep and valuable information from the input image
(2) We have developed a novel device for capturing high-quality contactless palmvein images
Summary
Personal authentication has become a vital and highly demanding technique which is the foundation of many applications, such as security access systems, time attendance systems, and forensics science [1]. The majority of methods in this field can be classified into three categories, line-like feature extraction, subspace feature learning and texture-based coding. Their results are unsatisfactory and have much room to improve. Different from these hand-crafted methods, deep convolutional neural networks (DCNN) can learn higher-level features from massive training samples via a deep architecture, and it can capture the representation for the task-specific knowledge Considering this point, we propose a DCNN-based scheme for the problem of palmprint/palmvein recognition, namely PalmRCNN (short for palmprint and palmvein recognition using CNNs), which can further extract the deep and valuable information from the input image. There are some approaches that are difficult to be categorized, such as [31,32], because they integrate various different processing methods together to extract palmprint features
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