Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A
- 1 June 2015
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1931-1939
- https://doi.org/10.1109/cvpr.2015.7298803
Abstract
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.Keywords
This publication has 10 references indexed in Scilit:
- Unconstrained Face Recognition: Identifying a Person of Interest From a Media CollectionIEEE Transactions on Information Forensics and Security, 2014
- A benchmark study of large-scale unconstrained face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- DeepFace: Closing the Gap to Human-Level Performance in Face VerificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- A Case Study of Automated Face Recognition: The Boston Marathon Bombings SuspectsComputer, 2013
- The challenge of face recognition from digital point-and-shoot camerasPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Open source biometric recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- The neural processing of familiar and unfamiliar faces: A review and synopsisBritish Journal of Psychology, 2011
- Face recognition in unconstrained videos with matched background similarityPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Attribute and simile classifiers for face verificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Robust Real-Time Face DetectionInternational Journal of Computer Vision, 2004