Asymmetric Joint Learning for Heterogeneous Face Recognition

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 intraclasses, 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-theart methods.

Authors

• Bing Cao Tianjin University Xidian University
• Nannan Wang Xidian University
• Xinbo Gao School of Electronic Engineering, Xidian University, Xi’an, China Chongqing University of Posts and Telecommunications, Chongqing, China
• Jie Li Shanghai Jiao Tong University Shanghai Jiao Tong University (Wuxi) Blockchain Advanced Research Center

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