Automatic analysis of facial beauty has become an emerging computer vision problem in recent years. Facial beauty prediction (FBP) aims at developing a human-like model that automatically makes facial attractiveness predictions. In this study, we present and evaluate a face attractiveness prediction approach using facial parts as well as a multi-task learning scheme. First, a deep convolutional neural network (CNN) pre-trained on massive face datasets is utilized for face attractiveness prediction, which is capable of automatic learning of high-level face representations. Next, we extend our deep model to other facial attribute recognition tasks. Hence, a multi-task learning scheme is leveraged by our deep model to learn optimal shared features for three correlated tasks (i.e. facial beauty assessment, gender recognition as well as ethnicity identification). To further enhance the attractiveness computation accuracy, specific regions of face images (i.e. left eye, nose and mouth) as well as the whole face are fed into multi-stream CNNs (i.e. three two-stream networks). Each two-stream network adopts a facial part as well as the full face as input. Extensive experiments are conducted on the SCUT-FBP5500 benchmark dataset, where our approach indicates significant improvement in accuracy over the other state-of-the-art methods.
Multi-Task Learning Via Co-Attentive Sharing For Pedestrian Attribute Recognition