Collaborative learning network for face attribute prediction

Abstract

This paper proposes a facial attributes learning algorithm with deep convolutional neural networks (CNN). Instead of jointly predicting all the facial attributes (40 attributes in our case) with a shared CNN feature extraction hierarchy, we cluster the facial attributes into groups and the CNN only shares features within each group in later feature extraction stages to jointly predicts the attributes in each group respectively. This paper also proposes a simple yet effective attribute clustering algorithm, based on the observation that some attributes are more collaborated (their prediction accuracy improve more when jointly learned) than others, and the proposed deep network is referred to as the collaborative learning network. Contrary to the previous state-of-the-art facial attribute recognition methods which require pre-training on external datasets, the proposed collaborative learning network is trained for attribute recognition from scratch without external data while achieving the best attribute recognition accuracy on the challenging CelebA dataset and the second best on the LFW dataset.