Correlation Filter Cascade for Facial Landmark Localization

Abstract

The application of correlation filters for the task of facial landmark detection has been studied by many vision works. Their success, however, is limited by the presence of large pose variations, expression and occlusion in face images. Moreover, existing correlation filters may suffer from poor discrimination to distinguish visually similar landmarks such as the right and left eyes. In this work, we present a new framework, referred to as Correlation Filter Cascade, to address the above limitations. The proposed framework consists of a set of correlation filters with different spatial supports (sizes) which are connected together in a cascade form. More specifically, the size of filters decreases from the lower to upper levels. Filters at lower levels implicitly code face shape information since they are trained using large patches stemmed from face images. This avoids ambiguous detections caused by landmarks with similar appearance. Detections in these levels, however, may not be accurate and suffer from small localization errors, mainly caused by face pose, expression and occlusion. Therefore, locations detected by lower levels will be further used by the higher levels to narrow down their search regions. Since the filters at higher levels have smaller size, they are less affected by pose, expression and occlusion, and thus, can perform more accurately. The evaluation on BioID and LFPW shows the superiority of our method compared to prior correlation filters and leading facial landmark detectors.