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<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems, different architectures can be considered by varying the positioning of the pair of cameras with respect to the face of the subject to be identified, as well as both kind and resolution of camera considered. These parameters can affect the correct decision rate of the system in classifying the input face, especially in presence of image uncertainty.<br />In this paper, several 3D architectures differing in camera specifications and geometrical positioning of the camera pair (with respect to the input face) are realized and compared. The detection of facial features in the images is made by adopting a popular method based on the Active Appearance Model (AAM) algorithm. 3D position of facial features is then obtained by means of stereo triangulation. The performance of the realized systems has been compared in terms of sensitivity to the quantities of influence and related uncertainty, and of typical indexes for the analysis of classification systems. Main results of such comparison show that the best performance can be reached by reducing the distance between cameras and subject to be identified and by minimizing the horizontal angle between the plane containing the camera pair axis and the face to be identified.</p>

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Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few. In such scenarios, several architectures based on both 2D image analysis and 3D reconstruction are investigated and proposed in literature. The actual performance of such systems in terms of correct decision rate is affected by several quantities of influence mainly concerning the conditions of acquisition of the image to be processed. As an example, the image luminosity, the lens defocus and the movement of a subject during the image acquisition can be sources of uncertainty which propagate up to the final classification result, thus affecting the reliability of a subject identification. In previous papers, the authors proposed suitable uncertainty models for both 2D and 3D based architectures able to quantify on-line the level of confidence to assign to the output of such systems according to the ISO-GUM. The proposed models required, for each quantity of influence, to estimate separately their deviations with respect to the reference values achieved in ideal acquisition conditions during the training phase. On the other hand, the quality of an image may be linked to the more general concept of signal-to-noise ratio (SNR), because noise affects the pixel of the image, thus introducing uncertainty on the final image. Therefore, looking for the development of a more straight and simple to use uncertainty model, in this paper the relationships among the quantities of influence and the image SNR are investigated. This activity represents the first step toward the realization of face-based recognition systems able to assign a level of confidence to the output results starting only from the evaluation of SNR on the input image