Topic-Aware Deep Auto-Encoders (Tda) For Face Alignment

Facial landmark localization plays an important role for many computer vision tasks, e.g., face recognition, face parsing, facial expression analysis, face animation, etc. However, it remains a challenging problem due to the diverse variations, such as head poses, facial expressions, occlusions and so on. In this work, we propose a topic-aware face alignment method to divide the difficult task of estimating the target shape into several much easier subtasks according to the topics. Specifically, topics are determined automatically by clustering according to the target shapes or shape deviations which are more compatible with the task of alignment. Then, within each topic, a deep auto-encoder network is employed to regress from the shape-indexed feature to the target shape. Deep model specific to each topic can capture more subtle variations in shape and appearance, and thus leading to better alignment results. This process is conducted in a cascade structure to further improve the performance. Experiments on three challenging databases demonstrate that our method significantly outperforms the state-of-the-art methods and performs in real-time.