工程科学与技术2024，Vol.56Issue(3)：240-248,9.DOI:10.15961/j.jsuese.202201010
虹膜与眼周深度特征融合网络模型
Deep Feature Fusion Network Model for Iris and Periocular Biometrics
摘要
Abstract
While iris recognition boasts a notably high recognition rate,unimodal biometrics suffer from vulnerabilities such as environmental factors and spoofing attacks.Particularly in remote or mobile settings with minimal constraints,the efficacy of iris recognition diminishes signific-antly.To address this challenge,leveraging periocular biometric features,situated proximately to the iris and known for their high discriminative power,presents a promising solution.By integrating iris and periocular features through bimodal fusion recognition,this issue can be effectively tackled.This paper introduces a novel approach for accurate and adaptive fusion recognition termed MultipleFusionNet,which incorporates iris and periocular depth features.Embracing the concept of channel attention and channel grouping attention,an automatic weight generation net-work is devised to dynamically assign weights to iris and periocular features through network learning.These weighted features,derived from convolutional neural networks,enable precise fusion and subsequently enhance recognition accuracy.The fusion module of this network model serves as a versatile deep feature fusion component,seamlessly integrable into any CNN backbone network,and characterized by its lightweight and straightforward implementation.Experimental validation is conducted using the CASIA-Iris-Distance dataset for remote scenarios and the CASIA-Iris-Lamp dataset for close-range illumination variations,both provided by the Chinese Academy of Sciences.Results from diverse com-parative and distance measurement experiments demonstrate the superiority of the proposed fusion model.Achieving a peak accuracy of 99.56%and a minimal Equal Error Rate(EER)of 0.002 7,measured by cosine distance,the model outperforms unimodal approaches and other related feature fusion methods.Furthermore,in terms of computational complexity,the proposed model exhibits efficiency,with a parameter count and computational workload approximately 1.5%lower than twice that of unimodal methods,and only 1%higher than standard fusion methods.These findings underscore the model's favorable balance of computational efficiency and performance prowess.关键词
双模态融合/虹膜识别/眼周识别/深度特征融合Key words
bimodal fusion/iris recognition/periocular recognition/deep feature fusion分类
信息技术与安全科学引用本文复制引用
雷松泽,李永刚,单奥奎,张文娟..虹膜与眼周深度特征融合网络模型[J].工程科学与技术,2024,56(3):240-248,9.基金项目
新型网络与检测控制国家地方联合工程实验室基金项目(GSYSJ2018002) （GSYSJ2018002）
陕西省自然科学基础研究项目(2020GY-066) （2020GY-066）
