基于改进局部协作表示的快速人脸识别算法  

Fast face recognition algorithm based on improved local collaborative representation

作　　者：施志刚[1] 邵冬华[2] 顾钦平 SHI Zhi-gang;SHAO Dong-hua;GU Qin-ping(Department of Management and Information,Nantong Shipping College,Nantong 226010,China;Educational Information Management Center,Nantong Shipping College,Nantong 226010,China)

机构地区：[1]南通航运职业技术学院管理信息系,江苏南通226010 [2]南通航运职业技术学院教育信息化管理中心,南通江苏226010

出　　处：《计算机工程与设计》2018年第9期2967-2973,共7页Computer Engineering and Design

基　　金：国家自然科学基金项目(61473159);南通航运学院科技基金重点基金项目(HYKJ/2016A02);江苏省大学生创业创新训练计划基金项目(201812703016Y)

摘　　要：为解决在大数据下基于协作表示的快速人脸识别问题,提出一种通过选择各类近邻达到类内平均值最相似的局部协作表示算法。在原始训练集上,选择各类与测试样本近邻的若干样本,将各类近邻样本平均值进行稀疏重构;将重构误差最小的若干类近邻局部样本协作表示,实现分类。该算法通过较少样本的协作表示在提高识别率的同时,进一步降低了运算的复杂度。仿真实验验证了所提方法运用于人脸识别中的有效性。To solve the problem of fast face recognition based on the collaborative representation under big data,the collaborative representation algorithm which achieved the maximum similarity of the intra class mean by selecting the nearest neighbors in each class was proposed.A certain number of training samples which was nearest to the testing sample in each class was selected from the original training set,and the mean of the nearest neighbor samples in each class collaboratively represented for reconstruction.According to the minimal reconstruction errors,the nearest neighbor local samples which belonged to a certain number of classes was collaboratively represented to realize the classification.This algorithm improves the recognition rate,and further reduces the complexity of computation through the collaborative representation of fewer samples.The results of the simulation experiments indicate the proposed method is effective in face recognition.

关 键 词：协作表示 快速 人脸识别 类内平均值最相似 局部样本 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]