Improvement of the Identification Rate using Finger Veins based on the Enhanced Maximum Curvature Method using Morphological Operators

All human biological traits are unique as biometrics, such as fingerprint, palm, iris, palm veins, finger veins and other biometrics. Using these biometrics has always been challenging. One of the challenges in biometrics is physical injuries. Finger vein biometrics is one of the characteristics that is most resistant to physical injuries. Numerous algorithms for authentication have been proposed with the help of this biometrics, which have weaknesses such as high computational complexity and low identification accuracy. In this paper, a new method in identification based on maximum curvature algorithm and morphological operators is proposed. The maximum curvature algorithm extracts image properties using a set of operations based on image returns. This process has been enhanced in the proposed method with morphological operators. What distinguishes the proposed method from other methods is that this algorithm is very accurate in distinguishing images which are similar but belonging to different classes. The proposed method, in addition to having a reasonable computational complexity, has been able to record very good identification accuracy in the challenge of low image quality. The identification accuracy of the proposed method is 97.5%, which compared to other methods has been able to improve more than 3%. Also, the identification speed of the proposed method is 0.84 seconds, which is very fast in its kind.