Palmprint classification using multiple advanced correlation filters and palm-specific segmentation

We propose a palmprint classification algorithm with the use of multiple correlation filters per class. Correlation filters are two-class classifiers that produce a sharp peak when filtering a sample of their class and a noisy output otherwise. For every class, we train the filters for a palm at different locations, where the palmprint region has a high degree of line content. With the use of a line detection procedure and a simple line energy measure, any region of the palm can be scored and the top-ranked regions are used to train the filters for each class. Using an enhanced palmprint segmentation algorithm, our proposed classifier achieves an average equal error rate of 1.12 x 10(-4)'% on a large database of 385 classes using multiple filters of size 64 x 64 pixels. The average false acceptance rate when the false rejection rate is zero is 2.25 X 10(-4)%.