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Face recognition using 3D structural geometry of rigid features extracted from 2D images

The problem of face recognition by computers has been the subject of over 40 years of research in computer vision, and a plethora of increasingly sophisticated techniques have been proposed throughout the years, with varying degrees of success. However, a definitive solution for uncontrolled, high-performance and/or high-precision environments has been elusive, mainly because most of these methods work on 2D images, which are mere projections of the the three-dimensional human face, thus being heavily variable in shape and appearance depending on pose, illumination and expressions of the face. More recently, 3D approaches have emerged, but despite good results, they present technical hurdles that 2D methods can avoid. This thesis aims to evaluate the hypothesis that rigid facial shape contributes significantly to the recognition problem. While many 2D approaches use shape-free patches for comparison, no approach has attempted partial shape normalization, cancelling facial expressionbased deformations, but preserving intrinsic proportions of the facial shape. We propose a hybrid 2D+3D recognition method on which we attempt to achieve this selective shape normalization by fitting the Candide 3D morphable face model into a 2D mesh generated through Active Shape Models, thus obtaining explicit estimates for pose, facial shape and facial expression. Our results show that normalizing pose and expression, with shape unmodified, increases performance in distinguishing faces.