Multi-Label Networks for Face Attributes Classification
Sara Atito Aly, Berrin A. Yanikoglu · 2018
Face attributes classification is drawing attention as a research topic with applications in multiple domains, such as video surveillance and social media analysis. In most attribute classification systems in literature, independent classifiers are trained separately for each attribute. In this work, we propose to train attributes in groups based on their localization (head, eyes, nose, cheek, mouth, shoulder, and general areas) in a multi-task learning scenario to speed up the training process and to prevent overfitting. We have evaluated the idea of using the location knowledge for a particular attribute group to speed up the network training. Attention is drawn to the area of interest by blurring training images outside the region of interest, fine-tuning the system and freezing the earlier layers before continuing training with original images. Several data augmentation techniques are also performed to reduce over-fitting. Our approach outperforms the state-of-the-art of the attributes on the public LFWA dataset, with an average improvement of almost 0.7% points. The accuracy ranges from 78% (detecting oval face or shadow on the face) to 97.4% (detecting blond hair) across the attributes.