Efficient Facial Attribute Recognition with a Spatial Codebook

There is a large number of possible facial attributes such as hairstyle, with/without glasses, with/without mustache, etc. Considering large number of facial attributes and their combinations, it is difficult to build attributes classifiers for all possible combinations needed in various applications, especially at the designing stage. To tackle this important and challenging problem, we propose a novel efficient facial attributes recognition algorithm using a learned spatial codebook. The Maximum Entropy and Maximum Orthogonality (MEMO) criterion is followed to learn the spatial codebook. With a spatial codebook constructed at the designing stage, attribute classifiers can be trained on demand with a small number of exemplars with high accuracy on the testing data. Meanwhile, up to 600 times speedup is achieved in the on-demand training process, compared to current state-of-the-art method. The effectiveness of the proposed method is supported by convincing experimental results.