Grip-Pattern Verification for Smart Gun Based on Maximum-Pairwise Comparison and Mean-Template Comparison
In our biometric verification system of a smart gun, the rightful user of a gun is authenticated by grip-pattern recognition. In this work verification will be done using two types of comparison methods, respectively. One is mean-template comparison, where the matching score between a test image and a subject is computed, by comparing the test image to the mean value of training samples of this subject. The other one is maximum-pairwise comparison, where the matching score between a test image and a subject is selected as the maximum, among all the similarity scores resulting from comparison between the test image and each training sample of this subject. Experimental results show that a much lower false-acceptance rate can be obtained at the required false-rejection rate of our system using maximum-pairwise comparison, than mean-template comparison.