A Novel Weightless Artificial Neural Based Multi-Classifier for Complex Classifications
Neural Processing Letters
Engineering Applications of Artificial Intelligence
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A majority of the existing fingerprint recognition algorithms are based on matching minutia features. Therefore, minutiae extraction is one of the critical steps in fingerprint verification algorithms. Poor quality fingerprint images lead to missing and spurious minutiae that degrate the performance of the matching system. We propose two new techniques for minutiae verification based on non-trivial gray level features. The proposed features intuitively represents the structual properties of the minutiae neighborhood leading to better classification. We use directionally selective steerable wedge filters to differentiate between minutiae and non-minutiae neighborhoods We also propose a second technique based on Gabor expansion that results in even better discrimination. We present an objective evaluation of both the algorithms.