LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, Boumerdes 35000, Algeria.
Electrical Engineering Department, University of Skikda, BP 26, El Hadaiek, Skikda 21000, Algeria.
Sensors (Basel). 2022 Dec 14;22(24):9814. doi: 10.3390/s22249814.
In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images' lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology-Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively.
近年来，掌纹识别作为一种可靠的个人身份识别方法，引起了越来越多的关注，并成为了研究的焦点。任何掌纹识别系统的性能主要取决于所采用的特征提取方法的有效性。在本文中，我们提出了一种三步法来解决非接触式掌纹识别的难题：（1）基于中值滤波和对比度受限自适应直方图均衡化（CLAHE）的预处理，用于去除潜在噪声并均衡图像的光照；（2）多分辨率分析应用于在几个离散小波变换（DWT）分辨率下提取二值化统计图像特征（BSIF）；（3）分类阶段使用基于 K-最近邻（K-NN）的分类器将提取的特征分类到相应的类别中。特征提取策略是这项工作的主要贡献；我们使用多分辨率分析从几个图像分辨率中提取相关信息，作为替代基于多补丁分解的经典方法。该方法使用印度理工学院德里分校（IITD）和中国科学院自动化研究所（CASIA）的两个非接触式掌纹数据库进行了全面评估。与当前的最先进方法相比，结果令人印象深刻：IITD 和 CASIA 数据库的排名第一识别率分别为 98.77%和 98.10%。
