Recognizing Human Faces: Physical Modeling and Pattern Classification
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Although significant work has been done in the field of face recognition, the performance of the state-of-the-art face recognition algorithms is not good enough to be effective in operational systems. Most algorithms work well for controlled images but are quite susceptible to changes in illumination, pose, etc. In this dissertation, we propose methods which address these issues, to recognize faces in more realistic scenarios. The developed approaches show the importance of physical modeling, contextual constraints and pattern classification for this task. For still image-based face recognition, we develop an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources, given just a single image. Though the problem is ill-posed in its generality, linear approximations to the subspace of Lambertian images in combination with rank constraints on unknown facial shape and albedo are used to make it tractable. In addition, we develop a purely geometric illumination-invariant matching algorithm that makes use of the bilateral symmetry of human faces. In particular, we prove that the set of images of bilaterally symmetric objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. For recognizing faces in videos, the challenge lies in suitable characterization of faces using the information available in the video. We propose a method that models a face as a linear dynamical system whose appearance changes with pose. Though the proposed method performs very well on the available datasets, it does not explicitly take the 3D structure or illumination conditions into account. To address these issues, we propose an algorithm to perform 3D facial pose tracking in videos. The approach combines the structural advantages of geometric modeling with the statistical advantages of a particle filter based inference to recover the 3D configuration of facial features in each frame of the video. The recovered 3D configuration parameters are further used to recognize faces in videos. From a pattern classification point of view, automatic face recognition presents a very unique challenge due to the presence of just one (or a few) sample(s) per identity. To address this, we develop a cohort-based framework that makes use of the large number of non-match samples present in the database to improve verification and identification performance.