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Proceedings Paper

Coupled discriminant mappings for heterogeneous face recognition
Author(s): Xinli Cao; Ke Wen; Likun Huang; Bing Tang; Wei Zhang
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Paper Abstract

Previous efforts on heterogeneous face recognition typically assume each subject has multiple training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a Single Sample Per Person (SSPP) exists in the training set. For face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. To solve this problem, we propose a novel learning-based algorithm called Coupled Discriminant Mapping (CDM) for heterogeneous face recognition. The CDM method finds a common space and learns a couple of discriminant projections for two different modalities without depending on the intra-class scatters. In the common space ,images of the same person are pulled into close proximity even if they come through different modalities meanwhile all the image under the same modality are pushed apart since each image belongs to a distinct class. The performance of CDM method is evaluated in two tasks: visual face image vs. near infrared face image and conventional face recognition. Experiments are conducted on two widely studied databases to show the effectiveness and consistence of the proposed CDM method.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300P (14 February 2020); doi: 10.1117/12.2538163
Show Author Affiliations
Xinli Cao, Wuhan Institute of Technology (China)
Ke Wen, Wuhan Institute of Technology (China)
Likun Huang, Wuhan Institute of Technology (China)
Bing Tang, Wuhan Institute of Technology (China)
Wei Zhang, 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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