Broad Siamese Network for Facial Beauty Prediction

Abstract

Facial beauty prediction (FBP) aims to automatically predict beauty scores of facial images according to human perception. Usually, facial images contain lots of information irrelevant to facial beauty, such as information about pose, emotion, and illumination, which interferes with the prediction of facial beauty. To overcome interferences, we develop a broad Siamese network (BSN) to focus more on the task of beauty prediction. Specifically, BSN consists mainly of three components: a multitask Siamese network (MTSN), a multilayer attention (MLA) module, and a broad representation learning (BRL) module. First, MTSN is proposed with different tasks about facial beauty to fully mine knowledge about attractiveness and guide the network to neglect interference information. In the subnetwork of MTSN, the MLA module is proposed to focus more on salient features about facial beauty and reduce the impact of interference information. Then, the BRL module based on broad learning system (BLS) is developed to learn discriminative features with the guidance of beauty scores. It further releases facial features from the impact of interference information. Comparisons with state-of-the-art methods demonstrate the effectiveness of BSN.