Multi-level Feature Learning for Face Recognition under Makeup Changes

Face recognition under makeup changes is challenging. In this paper, we propose a new hierarchical feature learning framework for face recognition under makeup changes. We observe that hierarchical structures exist among heterogeneous gaps. That is, features tend to be more invariant on the higher level, and less invariant on the lower level. To better model the structures of domain gaps, we seek for transformations of multi-level features by jointly learning level-wise transformations. Specifically, we adopt both strategies of feature-augmentation and feature-transformation, and combine them in a hierarchical framework. For the feature-augmentation strategy, a rich representation is assembled by features from coarse-to-fine levels. For the feature-transformation strategy, transformations are jointly learned to minimize the domain gap in each level. Further, to preserve the hierarchical structure of domain gap, level-wise regularizations are introduced to model the diverse heterogeneous patterns among different feature levels. Experiments show the superiority of our proposed approach over the state-of-the-art methods.