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Subject [ICPR 2021] Unsupervised Disentangling of Viewpoint and Residues Variations by Substituting Representations for Robust Face Recognition (by Minsu Kim) is accepted in ICPR 2021
Name IVY Lab. KAIST
Date 2020-10-16
Title: Unsupervised Disentangling of Viewpoint and Residues Variations by Substituting Representations for Robust Face Recognition
Authors: Minsu Kim, Joanna Hong, Junho Kim, Hong Joo Lee, Yong Man Ro
 
It is well-known that identity-unrelated variations (e.g., viewpoint or illumination) degrade the performances of face recognition methods. In order to handle this challenge, a robust method for disentangling the identity and view representations has drawn an attention in the machine learning area. However, existing methods learn discriminative features which require a manual supervision of such factors of variations. In this paper, we propose a novel disentangling framework through modeling three representations of identity, viewpoint, and residues (i.e., identity and pose unrelated) which do not require supervision of the variations. By jointly modeling the three representations, we enhance the disentanglement of each representation and achieve robust face recognition performance. Further, the learned viewpoint representation can be utilized for pose estimation or editing of a posed facial image. Extensive quantitative and qualitative evaluations verify the effectiveness of our proposed method which disentangles identity, viewpoint, and residues of facial images.