Hunting for Fashion via Large Scale Soft Biometrics Analysis

Fashion analysis has gained increasing attention thanks to its immense potential in fashion industry, precision marketing, and sociological analysis, etc. While a lot of fashion analysis work has been done for clothing and makeup, few of them address the problem from the perspective of large scale soft biometrics. In this paper, we focus on soft biometric attributes on human faces, particularly lip color and hair color, based on the analysis of which using a large scale data set we aim to reveal the fashion trend of lipstick color and hair color. To this end, we first perform the following steps on each image: face detection, occlusion detection, face parsing, and color feature extraction from the lip and hair regions. We then perform clustering based on the extracted color features in the given large scale data set. In the experiments, we collect from the Internet 15, 366 mouth-occluded and 14, 580 hair-occluded face images to train an effective occlusion detector such that noisy face images with occluded mouths/hairs are excluded from the subsequent fashion analysis, and another more than 20, 000 face images for analyzing the fashion trend of lipstick and hair colors. Our experimental results on the collected large scale data set prove the effectiveness of our proposed method.