QGFace: Quality-Guided Joint Training For Mixed-Quality Face Recognition
Youzhe Song Feng Wang
The quality of a face crop in an image is decided by many factors such as
camera resolution, distance, and illumination condition. This makes the
discrimination of face images with different qualities a challenging problem in
realistic applications. However, most existing approaches are designed
specifically for high-quality (HQ) or low-quality (LQ) images, and the
performances would degrade for the mixed-quality images. Besides, many methods
ask for pre-trained feature extractors or other auxiliary structures to support
the training and the evaluation. In this paper, we point out that the key to
better understand both the HQ and the LQ images simultaneously is to apply
different learning methods according to their qualities. We propose a novel
quality-guided joint training approach for mixed-quality face recognition,
which could simultaneously learn the images of different qualities with a
single encoder. Based on quality partition, classification-based method is
employed for HQ data learning. Meanwhile, for the LQ images which lack identity
information, we learn them with self-supervised image-image contrastive
learning. To effectively catch up the model update and improve the
discriminability of contrastive learning in our joint training scenario, we
further propose a proxy-updated real-time queue to compose the contrastive
pairs with features from the genuine encoder. Experiments on the low-quality
datasets SCface and Tinyface, the mixed-quality dataset IJB-B, and five
high-quality datasets demonstrate the effectiveness of our proposed approach in
recognizing face images of different qualities.