Improving Active Shape Models Robustness Towards Locating Facial Landmarks In Profile Contour

Active Shape Models (ASM) are widely explored for facial landmarks detection. The task is commonly approached in frontal perspective where target features such as eyes, mouth, and nose lie within the face's contour. Conversely, few application proposals are aimed to find facial landmarks in a profile view. This paper addresses an issue with that technique applied to locate facial landmarks lying on the profile contour. Those boundary points present an extra challenge for ASM's searching method when applied in images with background lighting variations. An ASM trained with profile images captured with different brightness backgrounds may lead the method to model texture profiles in segregated clusters. In those cases, it is inaccurate to assume that sampled texture profiles follow a Gaussian distribution, which is essential to estimate new landmarks locations with the Mahalanobis distance. We propose an alternative to improve ASM's robustness in such a scenario by utilizing K-Means to identify those clusters and create specific texture models for each one. Then, during the searching stage, new estimates are compared with each model individually, assuming the texture profiles follow a normal distribution inside their corresponding clusters. We trained and tested our improved ASM with a private database of 670 face images in profile view captured in a controlled environment. We report an average error reduction of 46% for landmarks location compared to the classical method. We also demonstrate that, regarding this issue, our proposal drives ASM's searching method to the point of convergence, whereas the original method does not.