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Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and(More)
As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for(More)
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