Facial landmark detection is a fundamental process included in many face analysis tasks. However, it faces great challenges for the facial images to be detected usually containing large variations, which will deteriorate the detection precision. Among such variations, partial occlusions and pose variations take great effects. In this study, the authors present a mixture of discriminative visibility-aware models (MDVMs) for facial landmark detection, to improve the generalisation ability of the model to occlusion and pose variation. By adopting different structure constrains for different poses as well as selecting different appearance model for the occluded parts, the MDVMs method can efficiently address the problem of partial occlusion and pose variation. Experiment results demonstrate that their proposed MDVMs method outperforms the well-known template-based methods, and can obtain much more accurate and robust facial landmarks detection results under both occlusions and pose variations.
['Yanfei Liu', 'Xiang-Dong Zhou', 'Yuanqian Li', 'Xiaohu Shao', 'X. Zhou']
Robust facial landmark detection using mixture of discriminative visibility-aware models