Cascaded Elastically Progressive Model for Accurate Face Alignment

Abstract

While recently published face alignment algorithms mainly focused on occlusion, low image quality, and complex head poses, subtle variances of facial components were often overlooked. In this correspondence paper, we propose a new approach called cascaded elastically progressive model aiming for pixel-wise landmark localization. First of all, elastically progressive model (EPM) is designed to synthesize the prior knowledge of face shape and appearance of test image. More specifically, a novel framework referred to as inherent linear structure (ILS) is explored for capturing the characteristics of the shape, which is more plastic and flexible than extensively used principle component analysis-based modeling. A locally linear support vector machine (LL-SVM) is used as local expert for searching candidate feature points. In order to optimally integrate ILS with localization results of LL-SVM, we introduce Kalman filter (KF) to dynamically estimate the true shape in the sense of least mean square error. Two schemes are utilized based on our modeling of KF. First, we embedded heuristic line-like search strategy into the framework to guarantee and accelerate the convergence. Second, Kalman gain is manipulated adaptively in accordance with the confidence of the localizers so that poorly localized points are more subject to global constraint than well localized ones. To further improve robustness to initializations, two EPMs are cascaded, in which primary EPM detects the global structure and secondary EPM captures the details. Validation experiments are conducted on in-the-wild LFPW and HELEN databases. Our method shows advantages for accurate landmark localization compared with prevailing methods.