Hybrid Face recognition using Fusion of DTCWT and FDCT Features

Abstract

The growth of automation in our daily life needs prime requirement of security. Biometric recognition or identification is one of the most important system for security and surveillance related activities compared to conventional techniques like ID cards, Personal Identification Number (PIN) and password. The biometric system security can be increased by combining multiple feature extraction algorithms rather than considering single feature extractor. The proposed work incorporates multi scale resolution techniques for extracting facial image features. The system independently incorporates Dual tree complex wavelet transform (DTCWT) technique to extract one set of features coefficients and Fast Discrete Curvelet Transform (FDCT) via wrapping to extract another set of face image features. The extracted features from both the techniques are multiplied and normalized to obtain final features. In order to classify the test feature with trained set of features Euclidean distance (ED) classifier is used. The system performance is evaluated for different face databases like FERET, L-space k, NIR and JAFFE. The values of False Acceptance Rate (FAR), False Rejection Rate (FRR), Total Success Rate (TSR) and Equal Error Rate (EER) were measured and it is found that the proposed system yields better recognition rate with minimal Equal Error Rate when compared to existing techniques.