Information Maximization for Extreme Pose Face Recognition

TL;DR

This paper introduces a novel coupled-encoder network that maximizes mutual information between frontal and profile face images in an embedding space, improving extreme pose face recognition.

Contribution

It proposes a pose-aware contrastive learning approach, a memory buffer for richer instance referencing, and a pose-aware adversarial domain adaptation for better cross-pose face matching.

Findings

Achieves superior accuracy on four benchmark datasets.

Outperforms state-of-the-art face recognition methods.

Demonstrates robustness to extreme pose variations.

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.