A robust face recognition approach through symbolic modeling of Polar FFT features

Abstract

Face recognition is a challenging and one of the most active area of research in computer vision. Variations in face image due to change in illumination, expression and presence of occlusions along with aging present many challenges to face recognition system. In this paper a new approach to robust face recognition using Polar FFT features modeled as symbolic data is proposed. Initially the face segment is cropped from the image using Viola-Jones algorithm and is converted into gray scale image of size 120 × 120 pixels. 2D-DFT is performed on pre-processed image. The dominant magnitude of 2D-DFT coefficients are computed using polar fourier transform technique and are represented as 1D P-FFT. The magnitude that represents maximum value in 1D P-FFT is considered as a feature value. The extracted feature value is used to construct a symbolic object to represent a face image. Further, a new symbolic similarity measure is devised and employed for assigning test symbolic object to a face class. The performance of the proposed method is evaluated with AR database and an average accuracy of 96.25% is achieved. 100% and 97% verification rate is achieved on the ORL and LFW face databases respectively. The various experiments conducted on AR, ORL and LFW databases using the proposed symbolic data modeling approach shows that this method has a very high degree of recognition and is better compared to some of the recently reported face recognition techniques.