Multiview Landmark Detection forIdentity-Preserving Alignment

Sammanfattning: Face recognition is a fundamental task in computer vision and has been an important field of study for many years. Its importance in activities such as face recognition and classification, 3D animation, virtual modelling or biomedicine makes it a top-demanded activity, but finding accurate solutions still represents a great challenge nowadays. This report presents a unified process for automatically extract a set of face landmarks and remove all differences related to pose, expression and environment by bringing faces to a neutral pose-centred state. Landmark detection is based on a multiple viewpoint Pictorial Structure model, which specifies first, a part for each landmark we want to extract, second a tree structure to constraint its position within the face geometry and third, multiple trees to model differences due the orientation. In this report we address both the problem of how to find a set of landmarks from a model and the problem of training such a model from a set of labelled examples. We show how such a model successfully captures a great range of deformations needing far less training examples than common commercial face detectors. The alignment process basically aims to remove differences between multiple faces so they all can be analysed under the same criteria. It is carried out with Thin-plate Splines to adjust the detected set of landmarks to the desired configuration. With this method we assure smooth interpolations while the subject identity is preserved by modifying the original extracted configuration of parts and creating a generic distribution with the help of a reference face dataset. We present results of our algorithms both in a constrained environment and in the challenging LFPW face database. Successful outcomes are shown that prove our method to be a solid process for unitedly recognise and warp faces in the wild and to be on a par with other state-of-the-art procedures.