Gradient Shape Model

Abstract

For years, the so-called Constrained Local Model (CLM) and its variants have been the gold standard in face alignment tasks. The CLM combines an ensemble of local feature detectors whose locations are regularized by a shape model. Fitting such a model typically consists of an exhaustive local search using the detectors and a global optimization that finds the CLM’s parameters that jointly maximize all the responses. However, one major drawback of CLMs is the inefficiency of the local search, which relies on a large amount of expensive convolutions. This paper introduces the Gradient Shape Model (GSM), a novel approach that addresses this limitation. We are able to align a similar CLM model without the need for any convolutions at all. We also use true analytical gradient and Hessian matrices, which are easy to compute, instead of their approximations. Our formulation is very general, allowing an optional 3D shape term to be seamlessly included. Additionally, we expand the GSM formulation through a cascade regression framework. This revised technique allows a substantially reduction in the complexity/dimensionality of the data term, making it possible to compute a denser, more accurate, regression step per cascade level. Experiments in several standard datasets show that our proposed models perform faster than state-of-the-art CLMs and better than recent cascade regression approaches.