Image worth Evaluation for False Biometric Detection: Submission to Iris, Fingerprint and Face Recognition

To ensure the actual presence of a real legitimate trait in contrast to a false self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. Present a new software-based false detection method that can be used in multiple biometric systems, face reorganization to detect different types of counterfeit access attempts. The idea of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, userfriendly, and non-intrusive manner, through the use of image quality measurement. The planned approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to differentiate between valid and impostor samples. The untried results, obtained on publicly available data sets of fingerprint, iris, and 2D face. To provide a forceful proof that a new method is better than the state-of-the-art, computer graphics projects are often accompanied by user studies, in which a group of observers rank or rate results of several algorithms. Such user studies, known as subjective image worth evaluation experiments, can be very time consuming and do not guarantee to produce conclusive results.