Face recognition by facial attribute assisted network

We propose the Facial Attribute Assistant Network (FAAN) for face recognition in the wild. The FAAN is developed to imitate some general human description of a face using facial attributes, such as gender, ethnicity, hair color and with or without eyeglasses. Given a face as the input, the FAAN renders an identity descriptor (I-descriptor) and an attribute descriptor (A-descriptor) in the output. These descriptors are exploited with the Minimum Distance Subset scheme, proposed in this study, for handling image-set based recognition. When searching for a match in a gallery set, only the subjects with similar attributes as of the probe, measured by the A-descriptors, are selected for matching by comparing the I-descriptors. The FAAN is built on the Residual Network (ResNet), which is first trained for face identification and then fine tuned for attribute identification. This study shows that attributes can effectively reduce the search space and make the search more efficient and accurate. Compared with other state-of-the-art methods, the FAAN demonstrates a competitive performance on the MPIE and IJB-A benchmarks.