International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Automated System for Recognizing Human Face Expression

Author : Meenakshi.R.P ¹ Krithika.R ² Mrs.V.Shivakrithika ³

Date of Publication :7th March 2015

Abstract: In this paper we introduce a new approach for facial expression recognition and emotional state recognition for human. 2D features were used in the existing system whereas the 3D features are used in the proposed system and dynamic analysis for natural interaction. In this survey automatic recognition is done through video sequence. The image processing is done by detecting the facial regions and 26 fiducial points are calculated which is taken as input frames. Based on the fiducial points facial expressions are recognized. Elastic Body Spline (EBS) is used for emotion classification with the feature extraction which depends on the 3Dmodel. This extracts the feature from realistic emotion expression and it is also applied in Driver’s Drowsiness Detection, Human Computer Interface, Psychological studies in Robotics which is automatically recognized through video sequence. The emotions are recognized from the fiducial points. Those emotions are taken as the input frame for human machine interface and Psychological studies in robotics as well as virtual reality. Facial scan technology acquires face from any static camera or video system that generates images of sufficient quality with high resolution. The facial expressions are recognized with the recognition rate average of 91% in the existing system. The recognized emotions are classified by using Elastic Body Spline(EBS) with the feature extraction which depends on the 3Dmodel. This extracts the feature from realistic emotion expression.

Reference :

1. "A Deformable 3-D Facial Expression Model for Dynamic Human Emotional State Recognition" Yun Tie, Member, IEEE, and Ling Guan, Fellow, IEEE VOL. 23, NO. 1, JANUARY 2013
2. "A deformable 3-d facial expression model for dynamic human emotional state recognition” yun tie, member, ieee, and ling guan vol. 23, no. 1, January 2013
3. www.face-rec.org/databases/
4. .L. D. Silva and S. C. Hui, “Real-time facial feature extraction and emotion recognition,” inProc. 4th Int. Conf. Inform. Commun. Signal Process., vol. 3. Dec. 2003, pp. 1310–1314 5. Computer Vision Pattern Recognition, Jun. 2006, pp. 1399–1406.
5. Stefano Arca, Raffaella Lanzarotti, and Giuseppe Lipori,"Face Recognition Based on 2D and 3D Features”
6. Wael Ben Soltana, Di Huang, Mohsen Ardabilian, Liming Chen,“2D&3D Features and Their Adaptive Score Level Fusion for 3D Face Recognition” 8.The xm2vts database. Web address: http://www .ee.survey .ac.uk/Research/VSSP/xm2vtsdb/
7. Face Recognition Using 2D and 3D Facial Data” Kyong I. Chang KevinW. Bowyer Patrick J. Flynn” Computer Science & Engineering Department University of Notre Dame Notre Dame, IN 46556 {kchang,kwb,flynn}@cse.nd.edu December. 2003