Regular Articles

Features classification using support vector machine for a facial expression recognition system

[+] Author Affiliations
Rajesh A. Patil, Vineet Sahula

Malaviya National Institute of TechnologyECE Department, JLN Marg, Jaipur 302017 India

Atanendu S. Mandal

CEERI Pilani, Rajasthan, India

J. Electron. Imaging. 21(4), 043003 (Oct 01, 2012). doi:10.1117/1.JEI.21.4.043003
History: Received February 28, 2012; Revised August 15, 2012; Accepted August 28, 2012
Text Size: A A A

Abstract.  A methodology for automatic facial expression recognition in image sequences is proposed, which makes use of the Candide wire frame model and an active appearance algorithm for tracking, and support vector machine (SVM) for classification. A face is detected automatically from the given image sequence and by adapting the Candide wire frame model properly on the first frame of face image sequence, facial features in the subsequent frames are tracked using an active appearance algorithm. The algorithm adapts the Candide wire frame model to the face in each of the frames and then automatically tracks the grid in consecutive video frames over time. We require that first frame of the image sequence corresponds to the neutral facial expression, while the last frame of the image sequence corresponds to greatest intensity of facial expression. The geometrical displacement of Candide wire frame nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to the SVM, which classify the facial expression into one of the classes viz happy, surprise, sadness, anger, disgust, and fear.

Figures in this Article
© 2012 SPIE and IS&T

Citation

Rajesh A. Patil ; Vineet Sahula and Atanendu S. Mandal
"Features classification using support vector machine for a facial expression recognition system", J. Electron. Imaging. 21(4), 043003 (Oct 01, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.4.043003


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.