Simultaneous Cascaded Regression
Pedro Martins, Jorge P. Batista · 2018
This paper addresses the problem of localizing facial landmarks with deformable face models using cascaded regression strategies. Recently, these methods have become quite popular, standing out as simple and efficient approaches to optimize nonlinear objective functions. In this paper, we target the well-known Lucas and Kanade (LK) image alignment formulation and introduce the Simultaneous Cascaded Regression (SCR) technique, which can be considered as a cascaded regression extension of the Simultaneous Forwards Additive / Inverse Composition approaches. In contrast to previous LK techniques (Newton based optimizations) which require to recompute Jacobian and Hessians matrices at each iteration, our approach learns (offline) a sequence of descent directions, effectively behaving as averaged steepest descent matrices. Under this revised technique, we propose a part-based generative model (with a linear warp function), that accounts with the underlying shape and appearance structure embedded into regression process itself. Our method is validated on a number of experiments on several datasets (LFPW, LFW, HELEN, 300W), demonstrating a noticeable gain in accuracy/fitting performance when compared with other face alignment solutions.