Unsupervised synchrony discovery in human interaction

Wen Sheng Chu, Jiabei Zeng, Fernando De La Torre, Jeffrey F. Cohn, Daniel S. Messinger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


People are inherently social. Social interaction plays an important and natural role in human behavior. Most computational methods focus on individuals alone rather than in social context. They also require labelled training data. We present an unsupervised approach to discover interpersonal synchrony, referred as to two or more persons preforming common actions in overlapping video frames or segments. For computational efficiency, we develop a branch-and-bound (B&B) approach that affords exhaustive search while guaranteeing a globally optimal solution. The proposed method is entirely general. It takes from two or more videos any multi-dimensional signal that can be represented as a histogram. We derive three novel bounding functions and provide efficient extensions, including multi-synchrony detection and accelerated search, using a warm-start strategy and parallelism. We evaluate the effectiveness of our approach in multiple databases, including human actions using the CMU Mocap dataset [1], spontaneous facial behaviors using group-formation task dataset [37] and parent-infant interaction dataset [28].

Original languageEnglish (US)
Title of host publication2015 International Conference on Computer Vision, ICCV 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages9
ISBN (Electronic)9781467383912
StatePublished - Feb 17 2015
Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
Duration: Dec 11 2015Dec 18 2015

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2015 International Conference on Computer Vision, ICCV 2015
ISSN (Print)1550-5499


Other15th IEEE International Conference on Computer Vision, ICCV 2015

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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