Min Max Threshold Range (MMTR) Approach in Palmprint Recognition

Palmprint recognition is an effective biometric authentication method to automatically identify a person’s identity. The features in a palmprint include principal lines, wrinkles and ridges etc. All these features are of different length and thickness. It is not possible to analyse them in single resolution, so multi-resolution analysis technique is required. Here, Wavelet transform is proposed as a multi-resolution technique to extract these features. Euclidian distance is used for similarity measurement. In addition, a Min Max Threshold Range (MMTR) method is proposed that helps in increasing overall system accuracy by matching a person with multiple threshold values. In this technique, firstly the person is authenticated at global level using Reference threshold. Secondly, the person is authenticated at local level using range of Minimum and Maximum thresholds defined for a person. Generally, personal authentication is done using reference threshold but there are chances of false acceptance. So, by using the Minimum and Maximum Thresholds range of false accepted persons at personal level, a person is identified to be false accepted or genuinely accepted. MMTR is an effective technique to increase the accuracy of the palmprint authentication system by reducing the False Acceptance Rate (FAR). Experimental results indicate that the proposed method improves the False Acceptance Rate drastically.