ClusterFace: Joint Clustering and Classification for Set-Based Face Recognition

TL;DR

This paper introduces a joint clustering and classification method for face recognition that improves robustness in unconstrained scenarios by analyzing feature space distributions before recognition.

Contribution

It proposes a hierarchical clustering approach that guides face classification, enhancing recognition accuracy under challenging conditions.

Findings

Outperforms state-of-the-art in face verification

Achieves competitive results in face identification

Improves rank-order search performance

Abstract

Deep learning technology has enabled successful modeling of complex facial features when high quality images are available. Nonetheless, accurate modeling and recognition of human faces in real world scenarios `on the wild' or under adverse conditions remains an open problem. When unconstrained faces are mapped into deep features, variations such as illumination, pose, occlusion, etc., can create inconsistencies in the resultant feature space. Hence, deriving conclusions based on direct associations could lead to degraded performance. This rises the requirement for a basic feature space analysis prior to face recognition. This paper devises a joint clustering and classification scheme which learns deep face associations in an easy-to-hard way. Our method is based on hierarchical clustering where the early iterations tend to preserve high reliability. The rationale of our method is that a reliable clustering result can provide insights on the distribution of the feature space, that can guide the classification that follows. Experimental evaluations on three tasks, face verification, face identification and rank-order search, demonstrates better or competitive performance compared to the state-of-the-art, on all three experiments.