Recently [15] proposed an approach for capturing a human similarity measure within a classifier, e.g., an artificial neural network, for face recognition. This is done by automatically generating and labeling arbitrarily large sets of morphed images (typically tens of thousands) in agreement with a human critic. One set is composed of images with reduced resemblance to the imaged person, yet recognizable by humans as that person (positive exemplars); the second set consists of images with some resemblance to the imaged person, but not enough to be recognizable as that person (negative exemplars). For each person of interest, a dedicated classifier is developed. From a practical point of view, it appears that the most challenging aspect of that approach is to completely enclose the decision region belonging to the person of interest. Because of the high dimensionality of the human face space, this is not simple matter especially for certain subjects. In this paper, we propose a new operator that morphs the image of the target person away from those of others. The new operator when applied together with the previous operator (morphing toward) helps to close the constructed decision region. Also, in this paper we propose the utilization of two networks for each target person; the added network covers not just the eyes and nose, but practically the entire face though in a coarse fashion. The second network, FaceNet, screens images before they are presented to the first network, EyeNet. The new developments have reduced the false accept rate by orders of magnitude with minimal impact on false reject rate. It now appears, more than before, that the following important and long desired goal is within reach: "The similarity measure used in a face recognition system should be designed so that humans' ability to perform face recognition and recall are imitated as closely as possible by the machine [5]".
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