Matching Algorithms for Image Recognition

We analyze the usage of matching algorithms for image recognition. We focus on the approaches which aim at finding nonlinear deformations of an entire image. ZeroOrder Warping (ZOW), Pseudo 2D Hidden Markov Model (P2DHMM) and Tree-Serial Dynamic Programming (TSDP) are studied. The effects of different constraints and parameter settings are analyzed. Furthermore, a new version of the TSDP and extensions for the P2DHMM are proposed. The proposed approaches allow to compensate for large disparities and additionally intend to preserve the monotonicity and continuity of the warping. The problem of local and global image variability occurs in many image recognition tasks and is a typical issue in the domain of face recognition. Many local deformations caused by changes in facial expression and pose make conventional distance functions fail. Additionally, face registration errors worsen the performance of most holistic methods. The P2DHMM is limited to a column-to-column mapping and is sensitive to registration errors, while the previously known version of the TSDP restricts the absolute displacement of each pixel. We propose to extend the P2DHMM by allowing deviations from a column which preserve the first-order dependencies between the pixels. Furthermore, we propose to relax some of the constraints imposed on the warping to cope with registration errors. A new version of the TSDP algorithm is proposed which relaxes the absolute constraints and intends to preserve the monotonicity and continuity of the warping. The proposed extensions are compared to the already known methods. Experimental results on the AR Face and the Labeled Faces in the Wild dataset show that the proposed approaches can outperform state-of-the-art methods.