DSA-Face: Diverse and Sparse Attentions for Face Recognition Robust to Pose Variation and Occlusion

Abstract

Learning local representations is important for face recognition (FR). Recent attention-based networks emphasize few facial parts, while ignoring other potentially discriminative ones. This is more serious when there are large pose variations, occlusions (e.g. face masks), or other image quality changes. To address this, we propose Diverse and Sparse Attentions, called DSA-Face. First, a divergence loss is designed to explicitly encourage the diversity among multiple attention maps by maximizing the Euclidean distance between every pair attention maps. As a result, a Pairwise Self-Contrastive Attention (PSCA) is developed to locate diverse facial parts which provide comprehensive descriptions. Second, an Attention Sparsity Loss (ASL) is proposed to encourage sparse responses in attention maps where only discriminative parts are emphasized while distracted regions (e.g. background or face masks) are discouraged. Built upon the PSCA and ASL, the DSA-Face model is developed to learn diverse and sparse attentions, which can extract diverse discriminative local representations and suppress the focus on noisy regions. Due to the pandemic of the COVID-19, the task of masked face matching is now very important, and our model can handle this much better than previous methods, demonstrating its effectiveness and usefulness. Moreover, our model outperforms the state-of-the-art methods on several other FR benchmarks, showing that it is also general to address various challenges in FR.

Topics & Concepts