Improved Sparse Representation Algorithm for Face Recognition Via Dense SIFT Feature Alignment

Zhou Quan; Wei Xin; Chen Jian-xin; Zheng Bao-yu

doi:10.11999/JEIT141194

Volume 37 Issue 8

Aug. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(8): 1913-1919

Zhou Quan, Wei Xin, Chen Jian-xin, Zheng Bao-yu. Improved Sparse Representation Algorithm for Face Recognition Via Dense SIFT Feature Alignment[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1913-1919. doi: 10.11999/JEIT141194

Citation:

Zhou Quan, Wei Xin, Chen Jian-xin, Zheng Bao-yu. Improved Sparse Representation Algorithm for Face Recognition Via Dense SIFT Feature Alignment[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1913-1919. doi: 10.11999/JEIT141194

Citation:

PDF( 3639 KB)

Improved Sparse Representation Algorithm for Face Recognition Via Dense SIFT Feature Alignment

doi: 10.11999/JEIT141194

Received Date: 2014-09-12
Rev Recd Date: 2015-04-24
Publish Date: 2015-08-19

Abstract

Abstract

In order to address the non-rigid deformation (e.g., misalignment, poses, and expression) of facial images, this paper proposes a novel sparse representation face recognition algorithm using Dense Scale Invariant Feature Transform (SIFT) Feature Alignment (DSFA). The whole method consists of two steps: first, DSFA is employed as a generic transformation to roughly align training and testing samples; and then, input facial images are identified based on proposed sparse representation model. A novel coarse-to-fine scheme is designed to accelerate facial image alignment. The experimental results demonstrate the superiority of the proposed method over other methods on ORL, AR, and LFW datasets. The proposed approach improves 4.3% in terms of recognition accuracy and runs nearly 6 times faster than previous sparse approximation methods on three datasets.
- Face recognition,
- Face alignment,
- Dense Scale Invariant Feature Fransform (SIFT) Feature,
- Sparse representation model

FullText(HTML)

References(28)

References

Li S Z and Jain A K. Handbook of face recognition[M]. New York, Springer, 2011: 1-374.

Yang A Y, Zihan Z, Ganesh B A, et al.. Fast-minimization algorithms for robust face recognition[J]. IEEE Transactions on Image Processing, 2013, 22(8): 3234-3236.

Cament A L, Castillo L E, Perez J P, et al.. Fusion of local normalization and Gabor entropy weighted features for face identification[J]. Pattern Recognition, 2014, 47(2): 568-577.

Jonathon P P and Alice O J. Comparison of human and computer performance across face recognition experiments[J]. Image and Vision Computing, 2014, 32(1): 74-85.

Radtke V W P, Granger E, Sabourin R, et al.. Skew-sensitive boolean combination for adaptive ensembles-An application to face recognition in video surveillance[J]. Information Fusion, 2014, 20(10): 31-48.

Abdullah M F A, Sayeed S M, Sonai K M, et al.. Face recognition with symmetric local graph Structure[J]. Expert Systems with Applications, 2014, 41(14): 6131-6137.

殷飞, 焦李成, 杨淑媛. 基于子空间类标传播和正则判别分析的单标记图像人脸识别[J]. 电子与信息学报, 2014, 36(3): 610-616.

Yin Fei, Jiao Li-cheng, and Yang Shu-yuan. Subspace label propagation and regularized discriminate analysis based single labeled image person face recognition[J]. Jounal of Electronics Information Technology, 2014, 36(3): 610-616.

赵振华郝晓弘. 局部保持鉴别投影及其在人脸识别中的应用[J]. 电子与信息学报, 2013, 35(2): 463-467.

Zhao Zhen-hua and Hao Xiao-hong. Linear locality preserving and discriminating projection for face recognition [J]. Jounal of Electronics Information Technology, 2013, 35(2): 463-467.

Ahonen T, Hadid A, and Pietikainen M. Face description with local binary patterns: Application to face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041

张洁玉, 赵鸿萍, 陈曙. 自适应阈值及加权局部二值模式的人脸识别[J]. 电子与信息学报, 2014, 36(6): 1327-1333.

Zhang Jie-yu, Zhao Hong-ping, and Chen Shu. Face recognition based on weighted local binary pattern with adaptive threshold[J]. Jounal of Electronics Information Technology, 2014, 36(6): 1327-1333.

Cootes T F, Edwards G J, and Taylor C J. Active appearance models[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(6): 681-685.

Belhumeur P N, Hespanha J P, and Kriegman D J. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711-720

Naseem I, Togneri R, and Bennamoun M. Linear regression for face recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(11): 2106-2112

Turk M and Pentland A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 2010, 3(1): 71-86.

Bartlett M S, Movellan J R, and Sejnowski T J. Face recognition by independent component analysis[J]. IEEE Transactions on Neuro Network, 2002, 13(6): 1450-1464.

Wright J, Yang A Y, Ganesh A, et al.. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227

Peng Y, Ganesh A, Wright J, et al.. Rasl: Robust alignment by sparse and low-rank decomposition for linearly correlated images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 22330-2246.

Wagner A, Wright J, Ganesh A, et al.. Toward a practical face recognition system: Robust alignment and illumination by sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(2): 372-386.

Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

Shekhovtsov A, Kovtun I, and Hlavac V. Efficient MRF Deformation Model for Non-Rigid Image Matching[C]. Proceedings of the IEEE Computer Vision and Pattern Recognition, Miami, FL, USA, 2007: 1-6.

Felzenszwalb P F and Huttenlocher D P. Efficient belief propagation for early vision[J]. International Journal of Computer Vision, 2006, 70(1): 41-54.

Boyd S and Vandenberghe L. Convex Optimization[M]. London, Cambridge University Press, 2004: 457-514.

Martinez A M. The AR face database[R]. CVC Tech. Rep. 1998.

Huang G B, Ramesh M, Berg T, et al.. Labeled faces in the wild: A database for studying face recognition in unconstrained environments[R]. University of Massachusetts, Amherst Tech. Rep. 7-49, 2007.

Viola P and Jones M J. Robust Real-Time Face Detection[J]. International Journal of Computer Vision, 2004, 57(3): 137-154.

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(11)

1.	郭鹏程，张文琪，李毅红. 基于跨层网络的危险物品X射线自动识别. 科学技术与工程. 2020(33): 13718-13724 .
2.	朱洋洋，贺兴时. 基于SIFT稀疏表示的人脸识别算法. 西安工程大学学报. 2020(06): 106-112 .
3.	仇荣超，娄树理，李廷军，宫剑. 多波段红外图像的海面舰船目标检测. 光谱学与光谱分析. 2019(03): 698-704 .
4.	江诚，石雄. 基于非线性特征提取的人脸识别算法研究. 武汉轻工大学学报. 2019(02): 35-39 .
5.	陈兵，查宇飞，李运强，张胜杰，张园强. 平移变化性相似性学习的行人重识别算法. 电子与信息学报. 2018(10): 2381-2387 . 本站查看
6.	郭少军，沈同圣. 模糊图像多波段特征融合的多类舰船识别. 激光与红外. 2017(02): 239-245 .
7.	何莉，罗艳芳. 基于数字图像处理技术的人脸检测算法研究. 计算机测量与控制. 2017(07): 273-275+281 .
8.	刘琨. 可远程监控的刷脸开锁保险箱的研究与开发. 计算机测量与控制. 2017(10): 94-97 .
9.	王玉伟，董西伟，陈芸. 基于稀疏表示的多模态生物特征识别算法. 计算机工程. 2016(10): 219-225 .
10.	刘洋，韩广良，史春蕾. 基于SIFT算法的多表情人脸识别. 液晶与显示. 2016(12): 1156-1160 .
11.	郝俊寿，丁艳会. 基于智能视觉的动态人脸跟踪. 现代电子技术. 2015(24): 12-15+18 .