Accuracy enhancement of biometric recognition using iterative weights optimization algorithm

Abstract

Abstract A new approach is proposed to quantitatively evaluate the binary detection performance of the biometric personal recognition systems. The importance of correlation between the overall detection performance and the area under the genuine acceptance rate (GAR) versus false acceptance rate (FAR) graph, commonly known as receiver operating characteristics (ROC) is recognized. Using the ROC curve, relation between GARmin and minimum recognition accuracy is derived, particularly for high security applications (HSA). Finally, effectiveness of any binary recognition system is predicted using three important parameters, namely GARmin, the time required for recognition and computational complexity of the computer processing system. The palm print (PP) modality is used to validate the theoretical basis. It is observed that by combining different useful feature-extraction techniques, it is possible to improve the system accuracy. An optimum algorithm to appropriately choose weights has been suggested, which iteratively enhances the system accuracy. This also improves the effectiveness of the system.

Keywords

• False acceptance rate (FAR)
• Genuine acceptance rate (GAR)
• Receiver operating characteristics (ROC)
• Area under the curve (AUC)
• Score level summing
• Optimal weights algorithm (OWA)