Facial expression recognition using depth map estimation of Light Field Camera

Tak Wai Shen, Hong Fu, Junkai Chen, W. K. Yu, C. Y. Lau, W. L. Lo, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

Facial expressions recognition has gained a growing attention from industry and also academics, because it could be widely used in many field such as Human Computer Interface (HCI) and medical assessment. In this paper, we evaluate the strength of the Light Field Camera for facial expression recognition. The light filed camera can capture the directions of the incoming light rays which is not possible with a conventional 2D camera. In addition, the light filed camera could estimates depth maps which provide further information to handle the facial expression recognition problem. Firstly, a new facial expression dataset is collected by the light field camera. The depth map is estimated and applied on Histogram Oriented Gradient (HOG) to encode these facial components as features. Then, a linear SVM is trained to perform the facial expression classification. Performance of the proposed approach is evaluated using the new dataset with estimated depth map. Experimental results show that significant improvements on accuracy are achieved as compared to the traditional approach.
Original languageEnglish
Title of host publicationICSPCC 2016 - IEEE International Conference on Signal Processing, Communications and Computing, Conference Proceedings
PublisherIEEE
ISBN (Electronic)9781509027088
DOIs
Publication statusPublished - 22 Nov 2016
Event2016 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2016 - City University of Hong Kong, Hong Kong, Hong Kong
Duration: 5 Aug 20168 Aug 2016

Conference

Conference2016 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2016
Country/TerritoryHong Kong
CityHong Kong
Period5/08/168/08/16

Keywords

  • facial component detection
  • facial expression recognition
  • HOG features
  • light field camera
  • SVM

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Instrumentation

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