Face Recognition Using Dense SIFT Feature Alignment

ZHOU Quan; Shafiq ur Rehman; ZHOU Yu; WEI Xin; WANG Lei; ZHENG Baoyu

doi:10.1049/cje.2016.10.001

Volume 25 Issue 6

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ZHOU Quan, Shafiq ur Rehman, ZHOU Yu, et al., “Face Recognition Using Dense SIFT Feature Alignment,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1034-1039, 2016, doi: 10.1049/cje.2016.10.001

Citation:

ZHOU Quan, Shafiq ur Rehman, ZHOU Yu, et al., “Face Recognition Using Dense SIFT Feature Alignment,” Chinese Journal of Electronics, vol. 25, no. 6, pp. 1034-1039, 2016, doi: 10.1049/cje.2016.10.001

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Face Recognition Using Dense SIFT Feature Alignment

doi: 10.1049/cje.2016.10.001

1.
Key Lab of Ministry of Education for Broad Band Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
2.
Department of Applied Physics and Electronics, Umeå University, Umeå, Sweden;
3.
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61201164, No.61201165, No.61201326, No.61271240, No.61401228, No.61571240, No.61501253), the State Key Development Program of Basic Research of China (No.2013CB329005), Jiangsu Postdoctoral Program (No.1201014C, No.1501019A), China Postdoctoral Science Foundation (No.2013M531392, No.2015M581841), Specialized Research Fund for the Doctoral Program of Higher Education (No.20113223120002), and University Natural Science Research Project of Jiangsu Province (No.11KJB510016).

Received Date: 2015-09-14
Rev Recd Date: 2015-12-10
Publish Date: 2016-11-10

Abstract

Abstract

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.
- Face recognition,
- Dense SIFT feature alignment,
- Sparse representation

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References(32)

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