Face Recognition in Unconstrained Environment

Abstract

The goal of computer vision is to provide the ability to machines to understand image data and infer the useful information from it. The inferences highly depend on the quality of the image data. But in many real-world applications, we encounter poor quality images which have low discriminative power which affects the performance of computer vision algorithms. In particular, in the field of Biometrics, the performance of face recognition systems are significantly affected when the face images have poor resolution and are captured under uncontrolled pose and illumination conditions as in surveillance settings. In this thesis, we propose algorithms to match the low-resolution probe images captured under non frontal pose and poor illumination conditions with the high-resolution gallery faces captured in frontal pose and good illuminations which are often available during enrollment. Many of the standard metric learning and dictionary learning approaches perform quite well in matching faces across different domains but they require the locations of several landmark points like corners of eyes, nose and mouth etc. both during training and testing. This is a difficult task especially for low-resolution images under non-frontal pose. In the first algorithm of this thesis, we propose a multi-dimensional scaling based approach to learn a common transformation matrix for the entire face which simultaneously transforms the facial features of the low-resolution and the high-resolution training images such that the distance between them approximates the distance had both the images been captured under the same controlled imaging conditions. It is only during the training stage that we need locations of different fiducial points to learn the transformation matrix. To overcome the computational complexity of the algorithm, we further proposed a reference-based face recognition approach with a trade-off on recognition performance...