Finger Knuckle Surface Print Verification using Gabor Filter

Abstract

The need for reliable user verification methods has increased due to severe security concerns. Hand-based biometrics plays an important role in providing security in real-time environments and are more successful in speed and accuracy. Finger knuckle images can also be used in forensic and criminal verification applications. This paper investigates an approach for personal verification using finger knuckle surface images. In this paper, after applying the pre-processing and noise reduction of finger knuckle images, by using Gabor filter extracting textural features from both proximal and distal phalanx knuckle regions. The textural features obtained from the Gabor filter are combined with the features of the gray-level co-occurrence matrix and finally classified by using K-nearest neighbor classifier and fuzzy K-nearest neighbor classifier. In the finger knuckle images database of 1435 Finger Knuckle print samples from 287 Fingers, we achieved an accuracy of 97.7% with fuzzy K-nearest neighbor classifier.