We investigate the problem of learning from a single instance consisting of a pair of images, often encountered in unconstrained face verification where the pair of images to be verified contain large variations and are captured from never seen subjects. Instead of constructing a separate discriminative model for each image in the couple and performing cross-checking, we learn a single Exemplar-SVM model for the pair by augmenting it with a negative couple set, and then predict whether the pair are from the same subject or not by asking an oracle whether this Exemplar-SVM is for a client or imposter in nature. The oracle by itself is learnt from the behaviors of a large number of Exemplar-SVMs based on the labeled background set. For face representation we use a number of unlabeled face sets collected from the Web to train a series of decision stumps that jointly map a given face to a discriminative and distributional representation. Experiments on the challenging Labeled Faces in the Wild (LFW) verify the effectiveness and feasibility of the proposed method.
CITATION STYLE
Song, F., & Tan, X. (2015). Learning one-shot exemplar SVM from the web for face verification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9005, pp. 408–422). Springer Verlag. https://doi.org/10.1007/978-3-319-16811-1_27
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