2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Abstract

Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

Survey of Palm Print Detection Techniques

“…And the highest accuracy they get is 97.32%. Jia and others [22] used CNN model to recognize the palm print. However, they didn't design the model manually.…”

Identity Recognition System Based on Multi-Spectral Palm Vein Image

“…For instance, Wu et al [1] selectively emphasize classification features using the SER model and weaken less useful features, thereby addressing issues related to rotation, translation, and scaling. Similarly, Wei et al [14] applied neural architecture search (NAS) techniques to overcome the drawbacks of manually designed CNNs, expanding the application of NAS technology in palm vein recognition. However, these methods may require large palm vein databases, limiting their applicability.…”

An effective hyperspectral palmprint identification system based on deep learning and band selection approach

“…Palmprint recognition has recently been identified as a novel biometric recognition technology, and it has received great interest. Palmprint recognition is the term for the technology that uses images of the palm skin on a human's hands to verify that the person is who they say they are [5]. The palmprint is of great interest to researchers because it is the most advanced biometric technique and has been used for over a century.…”