Verification of biometric palmprint patterns using optimal trade-off filter classifiers

We present results on classification of palmprint patterns from a large number of classes for biometric verification. We train optimal trade-off correlation filter classifiers with patterns of subregions of the palm as the actual biometric for the person's identity. Our results show that with less than 5 cm 2 (less than 1 in 2) of the actual palm captured at a low resolution, correlation filter algorithms can verify the authenticity of the palmprint pattern with error rates below 0.5% from as many as 400 different patterns. There is no previous work on biometric palmprint recognition that studies pattern verification of such small palmprint regions with such large number of classes.