Dual-Branch Partial Annotation Learning for Facial Attributes Recognition

Facial attribute recognition (FAR) aims to identify the attributes of a given face image. As a multi-label classification problem, conventional methods typically rely on large-scale fully annotated datasets. However, annotating all the facial attributes extensively is challenging and expensive, as totally tens of facial attributes can be defined and many of them are subtle or even vague thus requiring expertise for annotation. In contrast, it is much easier to annotate few (even one) most prominent attributes per face image, thus resulting in partially annotated dataset. To fully leverage this kind of datasets, we propose a novel pseudo-label based method named Dual-Branch Partial Annotation Learning (DB-PAL), in which two predicting branches respectively generate positive and negative annotations via loss-based ranking and validate each other to obtain better pseudo labels for training set augmentation. Extensive experiments on the CelebA and LFWA datasets demonstrate the superiority of our method.