Information Maximization for Extreme Pose Face Recognition

• URL: http://arxiv.org/abs/2209.03456v1
• Date: Wed, 7 Sep 2022 20:30:06 GMT
• Title: Information Maximization for Extreme Pose Face Recognition
• Authors: Mohammad Saeed Ebrahimi Saadabadi, Sahar Rahimi Malakshan, Sobhan Soleymani, Moktari Mostofa, and Nasser M. Nasrabadi
• Abstract summary: We exploit a connection using a coupled-encoder network to project frontal/profile face images into a common embedding space. The proposed model forces the similarity of representations in the embedding space by maximizing the mutual information between two views of the face.
• Score: 18.545778273606427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we seek to draw connections between the frontal and profile face images in an abstract embedding space. We exploit this connection using a coupled-encoder network to project frontal/profile face images into a common latent embedding space. The proposed model forces the similarity of representations in the embedding space by maximizing the mutual information between two views of the face. The proposed coupled-encoder benefits from three contributions for matching faces with extreme pose disparities. First, we leverage our pose-aware contrastive learning to maximize the mutual information between frontal and profile representations of identities. Second, a memory buffer, which consists of latent representations accumulated over past iterations, is integrated into the model so it can refer to relatively much more instances than the mini-batch size. Third, a novel pose-aware adversarial domain adaptation method forces the model to learn an asymmetric mapping from profile to frontal representation. In our framework, the coupled-encoder learns to enlarge the margin between the distribution of genuine and imposter faces, which results in high mutual information between different views of the same identity. The effectiveness of the proposed model is investigated through extensive experiments, evaluations, and ablation studies on four benchmark datasets, and comparison with the compelling state-of-the-art algorithms.

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