This paper evaluates the performance of classic CNNs in 2D and 3D palmprint recognition and palm vein recognition. Particularly, seventeen representative and classic CNNs are exploited for performance evaluation.
In the network and digital society, personal authentication is becoming a basic social service. It is well known that biometrics technology is one of the most effective solutions to personal authentication. In recent years, two emerging biometrics technologies, palmprint recognition and palm vein recognition have attracted a wide range of attention. Generally speaking, there are three subtypes of palmprint recognition technology, including 2D low resolution palmprint recognition, 3D palmprint recognition and high resolution palmprint recognition. High-resolution palmprint recognition is usually used for forensic applications. 2D low-resolution palmprint recognition and 3D palmprint recognition are mainly used for civil applications. This paper only focuses on civil applications of biometrics, therefore, the problem of highresolution palmprint recognition will not be investigated.
Many effective methods have been proposed for 2D low-resolution palmprint recognition (2D low-resolution palmprint recognition will be called 2D palmprint recognition for short in the rest of this paper), 3D palmprint recognition and palm vein recognition, which can be divided into two groups, i.e., traditional methods and deep learning-based methods.
In the past decade, deep learning has become the most important technology in the field of artificial intelligence. It has brought a breakthrough in performance for many applications, such as speech recognition, natural language processing, computer vision, image and video analysis, and multimedia. In the field of biometrics, especially in face recognition, deep learning has become the most mainstream technology. However, the research on deep learning-based 2D and 3D palmprint recognition and palm vein recognition is still very preliminary.
Convolution neural network (CNN) is one of the most important branches of deep learning technology, and has been widely used in various tasks of image processing and computer vision, such as target detection, semantic segmentation and pattern recognition. For image-based biometrics technologies, CNN is the most commonly used deep learning technique. Up to now, many classic CNNs have been proposed and impressive results have been achieved in many recognition tasks. However, the recognition performance of these classic CNNs for 2D and 3D palmprint recognition and palm vein recognition has not been systematically studied.
For example, existing deep learning-based palmprint recognition and palm vein recognition work only used simple networks, and did not provide an in-depth analysis. In the future, with the rapid development of CNNs, the recognition accuracy of new CNNs will be continuously improved. It can be predicted that CNNs will become one of the most important techniques for 2D and 3D palmprint recognition and palm vein recognition.
Therefore, it is very important to systematically investigate the recognition performance of classic CNNs for 2D and 3D palmprint recognition and palm vein recognition. To this end, this paper evaluates the performance of classic CNNs in 2D and 3D palmprint recognition and palm vein recognition. Particularly, seventeen representative and classic CNNs are exploited for performance evaluation.
The selected CNNs are evaluated on five 2D palmprint databases, one 3D palmprint database and two palm vein databases, all of which are representative databases in the field of 2D and 3D palmprint recognition and palm vein recognition. The five 2D palmprint databases include Hong Kong Polytechnic University palmprint II database (PolyU II), the blue band of the Hong Kong Polytechnic University multispectral (PolyU M_B) palmprint database, Hefei University of Technology (HFUT) palmprint database, Hefei University of Technology cross sensor (HFUT CS) palmprint database, and Tongji University palmprint (TJU-P) database. The 3D palmprint database we used is Hong Kong Polytechnic University 3D palmprint database (PolyU 3D). Two palm vein databases include the near-infrared band of Hong Kong Polytechnic University multispectral palmprint database (PolyU M_N) and Tongji University palm vein (TJU-PV) database.
It should be noted that the samples within the above databases are captured in two different sessions at certain time intervals. In traditional recognition methods, some samples captured in the first session are usually used as training sets, while all the samples captured in the second session are used as the test set. However, in existing deep learning-based palmprint recognition and palm vein recognition methods, the training set often contains samples from both sessions. Thus, it is easy to obtain a high recognition accuracy. If the training samples are only from the first session, and the test samples are from the second session, we call this experimental mode a separate data mode. If the training samples are from both sessions, we call this experimental mode a mixed data mode. We conduct experiments in both separate data mode and mixed data mode to observe the recognition performance of classic CNNs in these two different modes.
The main contributions of our work are as follows.
1) We briefly summarize the classic CNNs, which can help the readers to better understand the development history of CNNs for image classification tasks.
2) We evaluate the performance of the classic CNNs for 3D palmprint and palmprint recognition. To the best of our knowledge, it is the first time such an evaluation has been conducted.
3) We evaluated the performance of classic CNNs on Hefei University of Technology cross sensor palmprint database. To the best of our knowledge, it is the first time the problem of palmprint recognition across different devices using deep learning technology has been investigated.
4) We investigate the problem of the recognition performance of CNNs on both separate data mode and mixed data mode.
The rest of this paper is organized as follows. Section 2 presents the related work. Section 3 briefly introduces seventeen classic CNNs. Section 4 introduces the 2D and 3D palmprint and palm vein databases used for evaluation. Extensive experiments are conducted and reported in Section 5. Section 6 offers the concluding remarks.
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A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition
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