Who Missed the Class? -- Unifying Multi-Face Detection, Tracking and Recognition in Videos
Abstract
We investigate the problem of checking class attendance by detecting, tracking and recognizing multiple student faces in classroom videos taken by instructors. Instead of recognizing each individual face independently, first, we perform multi-object tracking to associate detected faces (including false positives) into face tracklets (each tracklet contains multiple instances of the same individual with variations in pose, illumination etc.) and then we cluster the face instances in each tracklet into a small number of clusters, achieving sparse face representation with less redundancy. Then, we formulate a unified optimization problem to (a) identify false positive face tracklets; (b) link broken face tracklets belonging to the same person due to long occlusion; and (c) recognize the group of faces simultaneously with spatial and temporal context constraints in the video. We test the proposed method on Honda/UCSD database and real classroom scenarios. The high recognition performance achieved by recognizing a group of multi-instance tracklets simultaneously demonstrates that multi-face recognition is more accurate than recognizing each individual face independently.
Recommended Citation
Y. Mao et al., "Who Missed the Class? -- Unifying Multi-Face Detection, Tracking and Recognition in Videos," Proceedings of the IEEE International Conference on Multimedia and Expo (2014, Chengdu, China), pp. 1 - 6, Institute of Electrical and Electronics Engineers (IEEE), Sep 2014.
The definitive version is available at https://doi.org/10.1109/ICME.2014.6890334
Meeting Name
IEEE International Conference on Multimedia and Expo (2014: Jul. 14-18, Chengdu, China)
Department(s)
Computer Science
Keywords and Phrases
Object Recognition; Optimization; Students; Tracking (Position); Context Constraint; Face Representations; Face Tracking; Multi-Object Tracking; Multiple Instances; Multiple Object Tracking; Number of Clusters; Unified Optimizations; Face Recognition; Face Detection; multiple Object Recognition
International Standard Serial Number (ISSN)
1945-7871
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Sep 2014
