Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Head matters : explainable human-centered trait prediction from head motion dynamics

Tools
- Tools
+ Tools

Madan, Surbhi, Gahalawat, Monika, Guha, Tanaya and Subramanian, Ramanathan (2021) Head matters : explainable human-centered trait prediction from head motion dynamics. In: 23rd ACM International Conference on Multimodal Interaction (ICMI '21), Montréal, QC, Canada, 18-22 Oct 2021. Published in: Proceedings of the 2021 International Conference on Multimodal Interaction pp. 435-443. ISBN 9781450384810. doi:10.1145/3462244.3479901

Research output not available from this repository.

Request-a-Copy directly from author or use local Library Get it For Me service.

Official URL: http://dx.doi.org/10.1145/3462244.3479901

Request Changes to record.

Abstract

We demonstrate the utility of elementary head-motion units termed kinemes for behavioral analytics to predict personality and interview traits. Transforming head-motion patterns into a sequence of kinemes facilitates discovery of latent temporal signatures characterizing the targeted traits, thereby enabling both efficient and explainable trait prediction. Utilizing Kinemes and Facial Action Coding System (FACS) features to predict (a) OCEAN personality traits on the First Impressions Candidate Screening videos, and (b) Interview traits on the MIT dataset, we note that: (1) A Long-Short Term Memory (LSTM) network trained with kineme sequences performs better than or similar to a Convolutional Neural Network (CNN) trained with facial images; (2) Accurate predictions and explanations are achieved on combining FACS action units (AUs) with kinemes, and (3) Prediction performance is affected by the time-length over which head and facial movements are observed.

Item Type: Conference Item (Paper)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Journal or Publication Title: Proceedings of the 2021 International Conference on Multimodal Interaction
Publisher: ACM
ISBN: 9781450384810
Book Title: Proceedings of the 2021 International Conference on Multimodal Interaction
Official Date: October 2021
Dates:
DateEvent
October 2021Published
18 October 2021Available
Page Range: pp. 435-443
DOI: 10.1145/3462244.3479901
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Conference Paper Type: Paper
Title of Event: 23rd ACM International Conference on Multimodal Interaction (ICMI '21)
Type of Event: Conference
Location of Event: Montréal, QC, Canada
Date(s) of Event: 18-22 Oct 2021
Related URLs:
  • Organisation

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us