Pattern recognition with SVM and dual-tree complex wavelets

A novel descriptor for pattern recognition is proposed by using dual-tree complex wavelet features and SVM. The approximate shift-invariant property of the dual-tree complex wavelet and its good directional selectivity in 2D make it a very appealing choice for pattern recognition. Recently, SVM has been shown to be very successful in pattern recognition. By combining these two tools we find that better recognition results are obtained. We achieve the highest rates when we use the dual-tree complex wavelet features with the Gaussian radial basis function kernel and the wavelet kernel for recognizing similar handwritten numerals, and when we use the Gaussian radial basis function for palmprint classification. Our findings are that the dual-tree complex wavelets are always better than the scalar wavelet for pattern recognition when SVM is used. Also, among many frequently used SVM kernels, the Gaussian radial basis function kernel and the wavelet kernel are the best for pattern recognition applications.