Accurate facial landmarks detection for frontal faces with extended tree-structured models

In this paper, we aim to improve one of the current state-of-the-art models for facial components detection/localization. The objectives are to increase the amount of landmark points detected and improve the landmark extraction accuracy for frontal faces. The model is following Zhu and Ramanan's approach with a tree-structure. The popular AR dataset is chosen as an alternative training dataset as it provides more landmark points requested. Our extension models are compared with Zhu and Ramanan's frontal face models in terms of detection accuracy. We also compare our models with another robust facial components detector called CompASM. Our experiments show that our models can achieve lower error rate on some fiducial points by providing more landmarks, and these accurate fiducial points will provide more accurate features for some applications related to facial shapes. The impact of image colour spaces other than RGB on the proposed detector is also investigated.