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 other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.

The number of images and videos available for search on the Internet is in the order of a trillion images, making current brute force search techniques prohibitively inefficient on such large scales. As society continues to increase our desire and ability to search these vast collections of data, improving upon traditional face recognition search techniques becomes an important problem to address. Because face recognition (and other biometric) algorithms are only commercially available as black box systems, any indexing scheme developed to perform efficient search must operate without access to the underlying feature vectors used to measure facial similarity. To address this restriction, we propose a structured search that separates the facial feature space into clusters derived from sets of prototype subjects we refer to as “synecdoches”. After an offline training step, our proposed method assigns each gallery image to a cluster in the face space based on its similarity to a set of synecdoche clusters. In turn, query images are compared to the target gallery images based on the closest synecdoche cluster in sequence. Our results show a minimal drop in accuracy when only considering half of the clusters, thus reducing the search space in half. Additional experiments demonstrate the viability of our proposed approach to improve search efficiency amidst the common restriction of a black box matcher.