Report on the FG 2015 video person recognition evaluation

• Main Authors: BEVERIDGE, J.R., ZHANG, H., DRAPER, B.A., FLYNN, P.J., FENG, Z., HUBER, P., KITTLER, J., HUANG, Zhiwu, LI S., LI Y., KAN, M., WANG, R., SHAN, S., CHEN, X., LI H., HUA, G., STRUC, V., KRIZAJ, J., DING, C., TAO, D.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2015
• Subjects: Cameras; Face recognition; Video cameras; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/6548; https://ink.library.smu.edu.sg/context/sis_research/article/7551/viewcontent/Report_on_the_FG_2015_Video_Person_Recognition_Evaluation.pdf
• Institution: Singapore Management University

This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second contained videos acquired from 5 different handheld video cameras. There were 1401 videos in each experiment of 265 subjects. The subjects, the scenes, and the actions carried out by the people are the same in both experiments. Five groups from around the world participated in the evaluation. The video handheld experiment was included in the International Joint Conference on Biometrics (IJCB) 2014 Handheld Video Face and Person Recognition Competition. The top verification rate from this evaluation is double that of the top performer in the IJCB competition. Analysis shows that the factor most effecting algorithm performance is the combination of location and action: where the video was acquired and what the person was doing.