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Algorithms based on face images are quite appealing for the possibility to easily adapt and tailor a system to many application domains. A system for personal identity verification and also recognition is presented. The core engine is a standard correlation-based matcher performed on iconic representations of face images. Through standard statistical tests of the recognition results obtained from two different data sets the actual physical limits of the pattern matcher are clearly shown. Successively also other aspects are taken into account, related to the feature space, allowing us to greatly improve the system performance reaching almost 100% correct recognition.