FRDF: Face Recognition Using Fusion of DTCWT and FFT Features

Abstract

Abstract Face recognition is a physiological Biometric trait widely used for personal authentication. In this paper we have proposed a technique for face recognition using Fusion of Dual Tree Complex Wavelet Transform (DTCWT) and Fast Fourier Transform (FFT) features. The Five Level DTCWT and FFT are applied on the pre-processed face image of size 128 × 512. The Five Level DTCWTfeatures are arranged in a single column vector of size 384 × 1. The absolute values of FFT features are computed and arranged in column vector of size 65, 536 × 1. The DTCWT features are fused with dominant absolute FFT values using arithmetic addition to generate a final set of features. The test image features are compared with database features using Euclidean distance to identify aperson. The face recognition is performed for different database such as ORL, JAFFE, L-SPACEK and CMU-PIE having different illumination and pose conditions. It is observed that the performance parameters False Acceptance Rate (FAR), False Rejection Rate (FRR) and True Success Rate (TSR) of proposed method FRDF: Face Recognition using Fusion of DTCWT and FFT are better compared to existing state of the art methods.