Sorting Pixels based Face Recognition Using Discrete Wavelet Transform and Statistical Features

Face recognition is used to identity a person effectively and most effective physiological biometric trait. In this paper, we propose sorting pixels-based face recognition using Discrete Wavelet Transform (DWT) and statistical features. The novel concept of sorting pixel values in ascending order is introduced and segmented into two parts viz., Low Pixel Values (LPV) and High Pixel Values (HPV). The DWT is applied on LPV matrix to generate low and high frequency bands such as LL, LH, HL and HH. The low frequency LL band is considered for features as the coefficient values are enhanced compared to original image pixel values and also reduction in dimensionality. The statistical measure is applied on HPV to compute mean, median, mode, maximum and standard deviation features. The features of LL band and statistical features are concatenated to obtain final features. The Artificial Neural Network (ANN) is used as classifier to recognize human beings. It is perceived that the performance of the proposed method is enhanced compared with the existing methods.