Asymmetric Joint Learning for Heterogeneous Face Recognition

Authors

  • Bing Cao Xidian University
  • Nannan Wang Xidian University
  • Xinbo Gao Xidian University
  • Jie Li Xidian University

Abstract

Heterogeneous face recognition (HFR) refers to matching a probe face image taken from one modality to face images acquired from another modality. It plays an important role in security scenarios. However, HFR is still a challenging problem due to great discrepancies between cross-modality images. This paper proposes an asymmetric joint learning (AJL) approach to handle this issue. The proposed method transforms the cross-modality differences mutually by incorporating the synthesized images into the learning process which provides more discriminative information. Although the aggregated data would augment the scale of intra-classes, it also reduces the diversity (i.e. discriminative information) for inter-classes. Then, we develop the AJL model to balance this dilemma. Finally, we could obtain the similarity score between two heterogeneous face images through the log-likelihood ratio. Extensive experiments on viewed sketch database, forensic sketch database and near infrared image database illustrate that the proposed AJL-HFR method achieve superior performance in comparison to state-of-the-art methods.

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Published

2018-04-27

How to Cite

Cao, B., Wang, N., Gao, X., & Li, J. (2018). Asymmetric Joint Learning for Heterogeneous Face Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12226