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Proceedings Paper

Improved modality-invariant feature learning for degraded face recognition
Author(s): Zhifang Yang; Zhengxin Song; Ke Wen
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Paper Abstract

In this paper, we investigate the degraded face recognition problem. At checkpoints, it is common that a passenger’s photo is digitally taken on the spot and compared with archived images scanned from printed photos. Therefore, the gallery set and the probe set come through two different media. The distortions introduced in the printing and the scanning processes often lead to unsatisfactory identification performance, necessitation further investigations in tackling degraded face recognition. Therefore, we propose an improved modality-invariant feature (IMIF) approach which combines the modality invariant features with a discriminative learning procedure to handle the variations in expression, occlusion and degradation. Experiments on the degraded face database show that the proposed IMIF enhances the degraded face recognition performance compared with other methods and validates the effectiveness of the proposed method.

Paper Details

Date Published: 14 February 2020
PDF: 6 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300X (14 February 2020); doi: 10.1117/12.2538253
Show Author Affiliations
Zhifang Yang, Wuhan Institute of Technology (China)
Zhengxin Song, Wuhan Institute of Technology (China)
Ke Wen, Wuhan Institute of Technology (China)

Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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