Patrick J. Grother

TL;DR: The humanlD gait challenge problem is introduced, to provide a means for measuring progress and characterizing the properties of gait recognition, and represents a radical departure from traditional computer vision research methodology.

TL;DR: Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.

TL;DR: The IARPA Janus Benchmark–C (IJB-C) face dataset advances the goal of robust unconstrained face recognition, improving upon the previous public domain IJB-B dataset, by increasing dataset size and variability, and by introducing end-to-end protocols that more closely model operational face recognition use cases.

TL;DR: Results show that recognition from indoor images has made substantial progress since FRVT 2000 and that three-dimensional morphable models and normalization increase performance and that face recognition from video sequences offers only a limited increase in performance over still images.

TL;DR: The IARPA Janus Benchmark-B (NIST IJB-B) dataset is introduced, a superset of IJB -A that represents operational use cases including access point identification, forensic quality media searches, surveillance video searches, and clustering.