Hybrid Domain Based Face Recognition System

Automation in every field of daily life is required the need of mechanized human identification and verification for ensuring the security. The study of physiological or the behavioural information is referred to as biometrics. Face recognition is a highly active research area with a wide variety application. In this paper, we propose Hybrid Domain Based Face Recognition System (HDFRS) for different databases. The original face image is resized to uniform dimensions of 2^p x 2^q. The DT-CWT of a signal x (n) is constructed using two critically-sampled DWTs in parallel with same data. The five levels Dual-Tree Complex Wavelet Transform (DT-CWT) is applied on face image to obtain DT-CWT coefficients. The matrix of DT-CWT coefficients is segmented in to 3x3 matrixes. The Local Binary Pattern (LBP) algorithm is applied on each 3x3 matrix to get final features. The Euclidean Distance (ED) is used to compare features of test face image with data base images. It is observed that the values of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are better in the proposed model compare to existing method.