采用强制稀疏编码的人脸识别方法  

Face Recognition Via Mandatory Sparse Coding

作　　者：石祥滨[1,2] 厉复圳 张德园[1] 

机构地区：[1]沈阳航空航天大学计算机学院,沈阳110136 [2]辽宁大学信息学院,沈阳110036

出　　处：《小型微型计算机系统》2017年第2期381-385,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(61170185)资助;航空科学基金项目(2013ZC54011)资助;辽宁省教育厅基金项目(L2014070)资助

摘　　要：针对稀疏表示重建精度高但计算速度慢,协同表示计算速度快但重建精度低的问题,提出一种兼顾重建精度与计算速度的算法,即强制稀疏编码算法.强制稀疏编码算法采用协同表示算法生成协同表示系数,并将其经强制稀疏处理后的结果作为稀疏表示算法迭代求解过程中的初始值和字典降维依据,从而达到通过设置倾向性初值和降低字典维数以提高求解稀疏表示速度的目的.在公开可获得的大量数据集上的实验表明,强制稀疏编码算法求解速度快,识别精度高,且所得编码向量稀疏度较高,在保证识别率的前提下,提高了计算效率.This paper proposed an algorithm,the mandatory sparse coding algorithm , which is based both on the accuracy of reconstruction and the computational speed to solve the problem that the high accuracy of reconstruction but the slow computation speed in sparse representation as well as the fast computation speed but the low accuracy of reconstruction in collaborative representation. The collaborative representation algorithm is used to generate the collaborative representation coefficient, the result dealt with the mandatory sparse coding algorithm is regarded as the criterion of the initial value and the dictionary dimension reduction in the iterative solution process of sparse representation in order to improve the speed of obtaining the solution of sparse representation with setting the tendentious initial value and reducing the dictionary dimensions. The proposed algorithm is proved to get a high accuracy with a fast solving process and a high sparsity of coding vector through the experiments on different data sets. It greatly improves the computational efficiency on the premise of the recognition accuracy is guaranteed.

关 键 词：稀疏表示 协同表示 强制稀疏编码 重建精度 计算效率 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]