LAUSR

Finger-vein verification is a considerable problem to be addressed in a biometric system. A lot of research works have been intended for finger-vein authentication with aid of diverse data mining algorithms. But, the verification accuracy using conventional algorithms was minimal. Also, the time complexity involved during the finger-vein verification was maximal. To overcome the above drawbacks, Steepest Deep Bipolar Cascade Correlative Machine Learning (SDBCCML) technique is proposed. The proposed SDBCCML technique is designed to efficiently perform the finger-vein verification process when considering the large size of the dataset as input. The proposed SDBCCML technique contains three main components namely input, hidden, and output units for effective finger-vein authentication. The input unit in the proposed SDBCCML technique takes a number of finger vein images as input and then sent it to the hidden units. The proposed SDBCCML technique employs more numbers of hidden units with aiming to deeply learn the input finger vein images and thereby find the significant vein features by using the Gabor filter. Subsequently, the discovered vein features at hidden units are forwarded to the output unit. In the proposed SDBCCML technique, the output unit applies a bipolar activation function that compares the extracted vein features with pre-stored templates in the dataset. After that, the output unit gives the verification result. If the output unit result is +1, then the input finger vein image is classified as an authorized person. If the output unit result is −1, then the input finger vein image is classified as an unauthorized person. Thus, the main contribution of the proposed SDBCCML technique increases the authentication performance of finger-vein with higher accuracy and minimal time. The simulation of the proposed SDBCCML technique is conducted using metrics such as accuracy, time complexity, error rate, F-Score, and space complexity for a diverse number of finger-vein images.