Occluded Face Pose Recognition Based on Dictionary Learning with Discrimination Performance

QIU Yiming; LIAO Haibin; CHEN Qinghu

doi:10.13203/j.whugis20150298

Volume 43 Issue 2

Feb. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(2): 275-281, 288. > DOI: 10.13203/j.whugis20150298

QIU Yiming, LIAO Haibin, CHEN Qinghu. Occluded Face Pose Recognition Based on Dictionary Learning with Discrimination Performance[J]. Geomatics and Information Science of Wuhan University, 2018, 43(2): 275-281, 288. DOI: 10.13203/j.whugis20150298

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Occluded Face Pose Recognition Based on Dictionary Learning with Discrimination Performance

1.
School of Electronic Information, Wuhan University, Wuhan 430072, China
2.
School of Computer Science and Technology, Hubei University of Science and Technology, Xianning 437100, China
3.
Jiangsu Jinling Sci & Tech Group Co., Ltd, Nanjing 210008, China

Funds:

The Program of the Natural Science Foundation of Hubei Province 2017CFB300

the Hubei Provincial Education Department Science and Technology Research Projects Q20172805

the Hubei Provincial Education Science Plan Project 2016GB086

More Information

Author Bio:
QIU Yiming, PhD, researcher, specializes in image processing and pattern recognition. E-mail: fhqim@sina.com
Corresponding author:
LIAO Haibin, PhD, associate professor. E-mail: liao_haibing@163.com
Received Date: March 28, 2016
Published Date: February 04, 2018

Graphical Abstract

Abstract

Abstract

This paper make full use of dictionary learning and sparse representation for signal reconstruction and classification, and present a two-step dictionary learning method and apply it to robust face pose recognition. The proposed approach models the appearance of face images from the subspace via K-SVD that learns the sub-dictionary from a set of images. A combination of the trained sub-dictionaries of all pose classes are used as an over-complete dictionary. Finally, the Gabor features are extracted for sparse representation and classification. In order to improve the classification ability, we put forward a two-step dictionary learning method, and carry out dictionary learning with label constraints in the second step. Additionally, in order to improve robustness against face occlusion, we introduce a pose occlusion dictionary to code the occluded portions of face images. Several experiments were performed on XJTU, PIE, and CAS-PEAL-R1 databases. Recognition results show that the proposed method can achieve a recognition rate of about 95% under illumination, noise, and occlusion variations. It can satisfy the requirements of practical applications.
- face pose recognition,
- face recognition,
- occlusion face processing,
- dictionary-learning,
- sparse representation

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References (21)

References

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