Statistical model and wavelet function for face recognition

In this study, we proposed two statistical methods were proposed to reduce the number of features extracted by using multi-level decomposition of wavelet transform applied on facial image to extract the significant features and it make the classification process less sensitive to variation in pose, expressions and light. All coefficients of wavelet transform that do not contribute for face classification have been deleted but the most appropriate wavelet coefficients required for classification were considered. Also using statistical parameters in order to determine whether the coefficients have to be removed or kept derives the error probability. The coefficients were kept or removed based on the threshold limits of the statistical parameters. The simplest classifier, Euclidean Distance Method (EDM) was used in the classification process. The experiments have been performed on Olivetti Research Laboratory database (ORL) and Yale University database (YALE) with different resolutions; success rate of up to 99.33 and 88.48% have been achieved on ORL and YALE database, respectively. These methods brought about 40% improvements in comparison to the method that accounts the biggest coefficients from the four levels of decompositions.