测试科学与仪器2025，Vol.16Issue(1)：85-95,11.DOI:10.62756/jmsi.1674-8042.2025009
基于CNN特征的协同稀疏表示人脸识别算法
Face recognition algorithm using collaborative sparse representation based on CNN features
摘要
Abstract
Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors,this study focuses on the improvement of feature extraction and model construction.Firstly,the convolutional neural network(CNN)features of the face are extracted by the trained deep learning network.Next,the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively,with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features.Finally,the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together.Based on this,the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set.The average recognition accuracy of this method is 94.45%on the CMU PIE face database and 96.58%on the AR face database,which is significantly improved compared with that of the traditional face recognition methods.关键词
稀疏表示/深度学习/人脸识别/字典更新/特征提取Key words
sparse representation/deep learning/face recognition/dictionary update/feature extraction引用本文复制引用
赵世林,徐成俊,刘昌荣..基于CNN特征的协同稀疏表示人脸识别算法[J].测试科学与仪器,2025,16(1):85-95,11.基金项目
We are grateful for the financial support from Natural Science Foundation of Gansu Province(Nos.22JR5RA217,22JR5RA216),Lanzhou Science and Technology Program(No.2022-2-111),Lanzhou University of Arts and Sciences School Innovation Fund Project(No.XJ2022000103),and Lanzhou College of Arts and Sciences 2023 Talent Cultivation Quality Improvement Project(No.2023-ZL-jxzz-03). （Nos.22JR5RA217,22JR5RA216）
