Toward A Human-Like Similarity Measure for Face Recognition

Abstract

We propose an approach for capturing a human similarity measure (within an artificial neural network, SVM, or other classifiers) for face recognition. That is, the following important and long desired goal appears achievable: "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". For each person of interest, a dedicated classifier is developed. Within the classifier we effectively capture a human classification functionality. This is done by automatically generating and labeling two arbitrarily large sets of morphed images (typically tens of thousands). 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 look-alikes, i.e. "others" who look almost like the imaged person (negative exemplars). Humans, unlike most face recognition systems, do not rank images as a precursor to recognition. Like humans, our system does not rank images, as it is capable of rejecting images of previously unseen faces (or faces which are not of interest) by simply examining their images, and recognizing faces for which it is trained to identify. We demonstrate this capability in our presented experiments, where a large set of impostor images that were not provided during training are consistently rejected by the system.