Influence Quantity Estimation in Face Recognition Digital Processing Algorithms

Nowadays, the matter of uncertainty in face recognition-based biometric systems is a relevant issue for the scientific community. This is due to the even more increasing deployment of such systems in critical applications such as safety, security, and access control, to cite a few. In this context, the authors are engaged in the design of general methods for uncertainty modeling and evaluation aimed at realizing face recognition-based biometric systems with built-in uncertainty evaluation capability. In this way, the output of a recognition system will not be the identity of the observed subject, but a confidence level for each possible subject. In previous papers the authors have identified the quantities of influence and have proposed a suitable uncertainty model. The core of the proposed model is the knowledge of the value assumed by the quantities of influence with respect to the corresponding values achieved in suitable reference conditions. This paper mainly analyzes these measurement issues, a fundamental step toward the development of such systems with built-in uncertainty evaluation capability. First results show a good agreement between statistical indicators and a priori estimations achieved with the proposed method.