Face Recognition Using Dense SIFT Feature Alignment

被引：0

作者：

ZHOU Quan ^{[1
]}

Shafiq ur Rehman ^{[2
]}

ZHOU Yu ^{[3
]}

WEI Xin ^{[1
]}

WANG Lei ^{[1
]}

ZHENG Baoyu ^{[1
]}

机构：

[1] Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology,Nanjing University of Posts and Telecommunications

[2] Department of Applied Physics and Electronics,Ume University

[3] School of Computer Science,Beijing University of Posts and Telecommunications

来源：

Chinese Journal of Electronics | 2016年 / 25卷 / 06期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

Face recognition; Dense SIFT feature alignment; Sparse representation;

D O I：

暂无

中图分类号：

TP391.41 [];

学科分类号：

080203 ;

摘要：

This paper addresses face recognition problem in a more challenging scenario where the training and test samples are both subject to the visual variations of poses, expressions and misalignments. We employ dense Scale-invariant feature transform(SIFT) feature matching as a generic transformation to roughly align training samples; and then identify input facial images via an improved sparse representation model based on the aligned training samples. Compared with previous methods, the extensive experimental results demonstrate the effectiveness of our method for the task of face recognition on three benchmark datasets.

引用

页码：1034 / 1039

页数：6

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