液晶与显示, 2020, 35 (2): 161, 网络出版: 2020-03-26   

基于叠加协同表示分类的人脸识别

Face recognition based on superposed collaborative representation based classification
作者单位
1 四川轻化工大学 自动化与信息工程学院, 四川 自贡 643000
2 成都工业学院 电子工程学院, 四川 成都 611730
3 电子科技大学 电子工程学院, 四川 成都 611731
摘要
针对叠加稀疏表示分类(SSRC)计算复杂度大的问题, 利用协同表示分类(CRC)的计算复杂度比SRC少得多且识别率相似于SRC的优点, 提出基于叠加协同表示分类(SCRC)的人脸识别。基于原型和变化的表示模型, 在SCRC中, 利用类质心和样本与质心的差异来构造成字典, 可以显著地改善CRC的性能。实验结果表明, 利用基于原型和变化的表示模型, 协同表示在人脸识别中能起作用, 甚至字典基在非受控和每类只有一个样本的条件下被汇集, 协同表示也具有很好的性能。与其他算法相比, SCRC在大幅降低计算复杂度的同时保证了识别率。
Abstract
For the problem of the high computational complexity of the superposed sparse representation based classification (SSRC), the advantages of the collaborative representation based classification (CRC) that has significantly less complexity than SRC is used and its recognition rate is similar to SRC. The face recognition based on superposed collaborative representation based classification (SCRC) is proposed. Based on a prototype plus variation model, the dictionary is assembled by the class centroids and the sample-to-centroid differences in the SCRC, which can leads to a substantial improvement on CRC. The experimental results show that, if the proposed prototype plus variation representation model is applied, the collaborative representation plays a crucial role in face recognition, and performs well even when the dictionary bases are collected under uncontrolled conditions and only a single sample per classes is available. Compared with the other algorithms, SCRC greatly reduces the computational complexity and ensures the recognition rates.

林国军, 蒋行国, 杨明中, 李兆飞, 解梅. 基于叠加协同表示分类的人脸识别[J]. 液晶与显示, 2020, 35(2): 161. LIN Guo-jun, JIANG Xing-guo, YANG Ming-zhong, LI Zhao-fei, XIE Mei. Face recognition based on superposed collaborative representation based classification[J]. Chinese Journal of Liquid Crystals and Displays, 2020, 35(2): 161.

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