Two-Dimensional Warping for Image Recognition

The task of image recognition is very challenging due to many intra-class variations. Especially the example of face recognition offers many challenges such as illumination, facial expressions, occlusions or different poses. Many different approaches have been proposed such as analyzing the sub-space spanned by face images, feature matching or three-dimensional models. However the approach of two-dimensional warping is particularly suited for this task. It defines a similarity measure between images that is very tolerant to local deformations and can be used for nearest-neighbor classification, which allows mug-shot recognition. In this thesis novel two-dimensional warping algorithms for image recognition within a nearest-neighbor classification framework are proposed. The new algorithms maintain full dependencies in both dimensions defined by a two-dimensional grid. This approach allows the enforcement of geometric constraints such as Sakoe constraints. This is implemented without sacrificing efficiency by relaxing the general two-dimensional warping criterion. During the alignment of columns a lookahead inspired by Tree-Serial Dynamic Programming (TSDP) is used. The developed algorithms are evaluated and compared to state-of-the-art warping methods on the task of face recognition. For this purpose the AR-Face and the CMU-PIE database are considered. To raise the level of difficulty, the former is altered by introducing artificial rotations. Experiments on both databases show the competitiveness of the novel algorithms and the effectiveness of the lookahead.