An adaptive-profile active shape model for facial-feature detection

In this paper, a novel algorithm based on the Active Shape Model (ASM) for locating landmarks on human faces is proposed. A challenge for detecting facial features is that faces may be under different poses, this makes the local appearance of each facial landmark vary greatly. To account for these variations, we propose an adaptive-profile scheme for ASM so that facial landmarks can be detected reliably and accurately under different poses. In our algorithm, a 2D profile is used for each landmark, and the 2D profiles of each landmark of the training face images are grouped to form a number of clusters. The corresponding shape vector for each of the clusters is then learned. For a query face image, the profiles to be used to locate the respective facial landmarks will be selected according to the face-shape vector in the current iteration. In other words, adaptive profiles are used in the search for landmarks. Face images from two subsets of the IMM Face Database are used for training, and the other two subsets are used for testing. The performance of our proposed algorithm is also evaluated using another dataset, namely the Bosphorus Dataset. Experiment results show that our proposed Adaptive-Profile Active Shape Model (APASM) can locate facial landmarks accurately under different face shapes, expressions, and poses.