Locating facial landmarks by support vector machine-based active shape model

Active shape model (ASM) plays an important role in face research such as face recognition, pose estimation and gaze estimation. A crucial step of the common ASM is finding a new position for each facial landmark at each iteration. Mahalanobis distance minimisation is used for this finding, provided there are enough training data such that the grey-level profiles for each landmark following a multivariate Gaussian distribution. However, this condition could not be satisfied in most cases. In this paper, a novel method support vector machine-based active shape model (SVMBASM) is proposed for this task. It approaches the finding task as a small sample size classification problem. Moreover, considering the poor classification performance caused by the imbalanced dataset which contains more negative instances (incorrect candidates for new position) than positive instances (correct candidates for new position), a multi-class classification framework is further proposed. Performance evaluation on Shanghai Jiao Tong University face database shows that the proposed SVMBASM outperforms the original ASM in terms of the average error and average frequency of convergence.