Linear shift-invariant maximum margin SVM correlation filter

Advanced correlation filters are effective for recognizing distorted images of a particular class. Most correlation filter designs are based on optimization criteria that lead to a closed form filter solution. We remove this restriction of a closed form solution and introduce a new filter design approach, based on a margin of separation maximization formulated as a linear support vector machine (SVM). The resulting SVM classifier is of the form of a correlation filter which has some attractive attributes, such as linearity and shift-invariance (properties that traditional SVM classifiers lack). We also show that our proposed SVM correlation filter offers built-in noise tolerance, which is valuable for any recognition task where noise can be present. More importantly, we demonstrate that we can achieve good generalization using only a single image for training. We compare our proposed filter design to popular advanced correlation filter designs and show the increase in performance of our proposed method by testing on two well known face databases (CMU-AMP lab facial expression database and the CMU-PIE illumination dataset consisting of faces of 65 people).