Efficient Application of Gabor Filters with Nonlinear Support Vector Machines

Both Gabor filters and Support Vector Machines (SVMs) are widely used in computer vision tasks for feature extraction and classification respectively. However the method is usually plagued by the problems of high computational complexity and memory usage owing to the high dimensionality of the Gabor filter responses. There were methods proposed to mitigate this problem by truncating or finding a gist of the responses but such approaches also lead to loss of information. Ashraf et al. gave a reinterpretation of the whole method and proposed a way to eliminate the need for such approximations. But they only give an analysis for linear SVM. This paper extends their work and provides analysis for nonlinear kernels within the same framework. The class of nonlinear kernels that are compatible with this framework are derived and experimental results on the facial expression recognition task are reported.