RECOGNITION OF IRIS & PALM-PRINT FOR SECURITY OF USER IDENTIFICATION

Palm-print and Iris are the effective methodology for personal authentication. Biometrics is a field of analysis of biological characteristics of an individual. Its purpose is to recognize and automatically verify the identity of a person based on physiological or behavioral characteristics. Multi-modal biometrics which combining several biometric based systems, are increasingly studied. Indeed, they reduce some limitations of unimodal based biometric systems, such as the inability to acquire data of individuals or intentional fraud, while improving the recognition performance. These benefits of multi-modality with unimodal biometric based systems are obtained by fusing multiple biometric traits. In this system, combining palm-print and Eye-Iris modalities to get the best of both worlds. They are recent and very important biometrics modalities due to their discriminatory power, robustness over time and there acceptability by users. To use, there characteristics must be extracted and enrolled for future comparison. The present proposed method is based on Histogram of Oriented Gradients (HOG) and Random Forest Transform (RFT) in order to improve the performance of the multi-modal biometric system based on palm-print and iris scan modalities. The results of the different classifiers are combined (fused) at the matching score level.