Quantum Estimation of Adaptive Local Binary Pattern for Authentication Based on Finger Knuckle Print

The content based image retrieval searches digital images in a large image database and uses visual content of images instead of metadata. This approach has many usages in security and authentication for example scanning the iris, fingerprint or finger knuckle print. This paper contributes a new method for personal authentication using finger knuckle print based on a new local binary pattern and image segmentation. The capabilities of quantum science lead to take its advantage in different areas of image processing. The main idea is inspired by the theory of quantum estimation and is applied to the feature extraction phase, in addition, the quantum circuit of the proposed feature is also designed. In order to measure the efficiency and accuracy of the proposed method, the EER (Equal Error Rate) is calculated. After implementing the proposed algorithm on the POLYU dataset, which contains 7920 images, the EER = 0.67 and accuracy =99% are obtained which indicate that the new method has more efficiency and accuracy than the similar approaches.