Face Attributes Recognition Based on One-Way Inferential Correlation Between Attributes

Attributes recognition of face in the wild is getting increasingly attention with the rapid development of computer vision. Most prior work tend to apply separate model for the single attribute or attributes in the same region, which easily lost the information of correlation between attributes. Correlation (e.g., one-way inferential correlation) between face attributes, which is neglected by many researches, contributes to the better performance of face attributes recognition. In this paper, we propose a face attributes recognition model based on one-way inferential correlation (OIR) between face attributes (e.g., the inferential correlation from goatee to gender). Toward that end, we propose a method to find such correlation based on data imbalance of each attribute, and design an OIR-related attributes classifier using such correlation. Furthermore, we cut face region into multiple region parts according to the category of attributes, and use a novel approach of face feature extraction for all regional parts via transfer learning focusing on multiple neural layers. Experimental evaluations on the benchmark with multiple face attributes show the effectiveness on recognition accuracy and computational cost of our proposed model.