BioNet: A Biologically-Inspired Network for Face Recognition

Recently, whether and how cutting-edge Neuroscience findings can inspire Artificial Intelligence (AI) confuse both communities and draw much discussion. As one of the most critical fields in AI, Computer Vision (CV) also pays much attention to the discussion. To show our ideas and experimental evidence to the discussion, we focus on one of the most broadly researched topics both in Neuroscience and CV fields, i.e., Face Recognition (FR). Neuroscience studies show that face attributes are essential to the human facerecognizing system. How the attributes contribute also be explained by the Neuroscience community. Even though a few CV works improved the FR performance with attribute enhancement, none of them are inspired by the human facerecognizing mechanism nor boosted performance significantly. To show our idea experimentally, we model the biological characteristics of the human face-recognizing system with classical Convolutional Neural Network Operators (CNN Ops) purposely. We name the proposed Biologically-inspired Network as BioNet. Our BioNet consists of two cascade sub-networks, i.e., the Visual Cortex Network (VCN) and the Inferotemporal Cortex Network (ICN). The VCN is modeled with a classical CNN backbone. The proposed ICN comprises three biologicallyinspired modules, i.e., the Cortex Functional Compartmentalization, the Compartment Response Transform, and the Response Intensity Modulation. The experiments prove that: 1) The cutting-edge findings about the human facerecognizing system can further boost the CNN-based FR network. 2) With the biological mechanism, both identityrelated attributes (e.g., gender) and identity-unrelated attributes (e.g., expression) can benefit the deep FR models. Surprisingly, the identity-unrelated ones contribute even more than the identity-related ones. 3) The proposed BioNet significantly boosts state-of-the-art on standard FR benchmark datasets.