Intelligent face mask prediction system

Biometric based person identification systems are used to provide alternative solutions for security. Although many approaches and algorithms for biometric recognition techniques have been developed and proposed in the literature, relationships among biometric features have not been studied in the field so far. In this study, we have analysed the existence of any relationship between biometric features and we have tried to obtain a biometric feature of a person from another biometric feature of the same person. Consequently, we have designed and introduced a new and intelligent system using a novel approach based on artificial neural networks for generating face masks including eyes, nose and mouth from fingerprints with 0.75-3.60 absolute percent errors. Experimental results have demonstrated that it is possible to generate face masks from fingerprints without knowing any information about faces. In addition it is shown that fingerprints and faces are related to each other closely. In spite of the proposed system is initial study and it is still under development, the results are very encouraging and promising. Also proposed work is very important from view of the point that it is a new research area in biometrics.