Personal authentication based on finger knuckle print using quantum computing

The finger knuckle print (FKP) images are used for personal authentication. The proposed model consists of pre-processing of the FKP image and then feature extraction algorithm is applied to extract coefficients that will be used in the matching process. In the classification process, improved versions of neural networks [quantum neural network (QNN), wavelet neural network (WNN) and quantum wavelet neural network (QWNN)] are used to approach better accuracy and speed of convergence. This paper has precedence in implementation of the quantum computing (QC) in the structure of the FKP recognition system. It has advantages of low inexactness and high speed of execution by using the quantum superposition state ideology. A database gathered from 165 volunteers by Hong Kong Polytechnic University (Poly U) and the proposed authentication model performance is tested upon it. Compared with other existing FKP recognition systems, the proposed one has merits of more secure as well as high accuracy and speed.