Analysis and evaluation of SURF descriptors for automatic 3D facial expression recognition using different classifiers

Amal Azazi, Syaheerah Lebai Lutfi, Ibrahim Venkat

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

4 Citations (Scopus)

Abstract

Emotion recognition plays a vital role in the field of Human-Computer Interaction (HCI). Among the visual human emotional cues, facial expressions are the most commonly used and understandable cues. Different machine learning techniques have been utilized to solve the expression recognition problem; however, their performance is still disputed. In this paper, we investigate the capability of several classification techniques to discriminate between the six universal facial expressions using Speed Up Robust Features (SURF). The evaluation were conducted using the BU-3DFE database with four classifiers, namely, Support Vector machine (SVM), Neural Network (NN), k-Nearest Neighbors (k-NN), and Naïve Bayes (NB). Experimental results show that the SVM was successful in discriminating between the six universal facial expressions with an overall recognition accuracy of 79.36%, which is significantly better than the nearest accuracy achieved by Naïve Bayes at significance level p < 0.05.

Original languageEnglish
Title of host publication2014 4th World Congress on Information and Communication Technologies, WICT 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages23-28
Number of pages6
ISBN (Electronic)9781479981151
DOIs
Publication statusPublished - 1 Apr 2014
Externally publishedYes
Event2014 4th World Congress on Information and Communication Technologies, WICT 2014 - Melaka, Malaysia
Duration: 8 Dec 201411 Dec 2014

Conference

Conference2014 4th World Congress on Information and Communication Technologies, WICT 2014
CountryMalaysia
CityMelaka
Period8/12/1411/12/14

Keywords

  • 3D Facial Expression Recognition
  • Human-computer interaction
  • k-nearest neighbors
  • Naïve Bayes
  • Neural Network
  • Support Vector machine

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