Dual Transform based Feature Extraction for Face Recognition

Face recognition is a biometric tool for authentication and verification, has great emphasis in both research and practical applications. Increased requirement on security, fully automated biometrics on personal identification and verification has received extensive attention over the past few years. In this paper Dual Transform based Feature Extraction for Face Recognition (DTBFEFR) is proposed. The images from database are of different size and format, and hence are to be converted into standard dimension, which is appropriate for applying DT-CWT. Variation due to expression and illumination are compensated by applying DWT on an image and also DWT reduces image dimension by decomposition. The DT-CWT is applied on LL subband, which is generated after two-level DWT, to generate DT-CWT coefficients to form feature vectors. The feature vectors of database and test face are compared using Random Forest, Euclidian Distance and Support Vector Machine matching algorithms. The correct recognition rate, false reject rate, false acceptance rate and efficiency are better in the case of proposed method as compared to existing techniques.