文献类型：期刊文献

英文题名：CECNet: Coordinate Encoding Competitive Neural Network For Palm Vein Recognition by Soft Large Margin Centralized Cosine Loss

作者：Zhang, Menghan[1];Li, Ji[1];Wang, Yifan[1];Xu, Gang[2]

第一作者：Zhang, Menghan

通讯作者：Li, J[1]

机构：[1]Guilin Univ Elect Technol, Sch Comp & Informat Secur, Guilin 541004, Peoples R China;[2]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450016, Peoples R China

第一机构：Guilin Univ Elect Technol, Sch Comp & Informat Secur, Guilin 541004, Peoples R China

通讯机构：[1]corresponding author), Guilin Univ Elect Technol, Sch Comp & Informat Secur, Guilin 541004, Peoples R China.

年份：2023

卷号：11

起止页码：141329-141342

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20235115238124);Scopus(收录号：2-s2.0-85179812990);WOS:【SCI-EXPANDED(收录号:WOS:001126240400001)】；

基金：No Statement Available

语种：英文

外文关键词：Palm vein recognition; coordinate encoding competitive neural network; soft large margin centralized cosine loss; adaptive Gabor filter encoders; feature extraction

摘要：Palm vein recognition plays a crucial role in identity verification, requiring highly discriminative features. However, for touchless palm vein datasets, selecting a suitable and fixed Region of Interest (ROI) is challenging due to variations in capture scales and anisotropy. Moreover, manually annotating ROIs for each palm vein image is a time-consuming and labor-intensive task. To address these challenges, the method based on competitive mechanism is currently a popular approach for palm vein recognition. However, traditional competitive mechanisms only focus on selecting winners from different channels without considering the spatial information of features. In this paper, we reformulate the traditional competition mechanism and propose a Coordinate Encoding Competitive Neural Network (CECNet). Our method takes into account the spatial competition relationship between features, which means we pay attention to features of different directions and scales. We also perform spatial encoding on the competitive features to extract a more comprehensive set of competitive features. To extract the textures, the CECNet employs three parallel Adaptive Gabor Filter Encoders (AGFEs) to learn features of different directions and scales, effectively capturing the variations present in palm vein images. To enhance feature discrimination, the Soft Large Margin Centralized Cosine Loss (SLMCCL) function is utilized, taking into account with inter-class separation and introducing centralized cosine similarity to achieve better intra-class similarity. By optimizing this loss function, the network learns to prioritize and rank features based on their importance. Experimental results on public palm vein datasets demonstrate the effectiveness of the proposed approach.