Facial Expression Generated Automatically Using Triangular Segmentation

Abstract:

The advancement in computer technology provided instant messaging software that makes human interactions possible and dynamic. However, such software cannot convey actual emotions and lack a realistic depiction of feelings. Instant messaging will be more interesting if users’ facial images are integrated into a virtual portrait that can automatically create images with different expressions. This study uses triangular segmentation to generate facial expressions. The application of an image editing technique is introduced to automatically create images with expressions from an expressionless facial image. The probable facial regions are separated from the background of the facial image through skin segmentation and noise filtration morphology. The control points of feature shapes are marked on the image to create facial expressions. Triangular segmentation, image correction, and image interpolation technique are applied. Image processing technology is also used to transform the space of features, thus generating a new expression.

• Periodical: Applied Mechanics and Materials (Volumes 479-480)
• Pages: 834-838
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.479-480.834
• Online since: December 2013
• Authors: Jia Shing Sheu, Tsu Shien Hsieh, Ho Nien Shou
• Keywords: Expression Generation, Feature Extraction, Feature Localization, Image Correction, Triangular Segmentation

References

[1] A. Khanam and M. Mufti, Intelligent Expression Blending for Performance Driven Facial Animation, IEEE Transactions on Consumer Electronics, vol. 53, no. 2, (2007), pp.578-583.

DOI: 10.1109/tce.2007.381732

[2] Z. Liu, Y. Shan, and Z. Zhang, Expressive Expression Mapping with Ratio Images, Proc. ACM SIGGRAPH, Los Angeles, CA, (2001), pp.271-276.

[3] P. S. Aleksic and A. K. Katsaggelos, Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs, IEEE Transactions on Information Forensics and Security, vol. 1, no. 1, (2006), pp.3-11.

DOI: 10.1109/tifs.2005.863510

[4] S. L. Phung, A. Bouzerdoum, and D. Chai, Skin Segmentation Using Color Pixel Classification: Analysis and Comparison, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, (2005), pp.148-154.

DOI: 10.1109/tpami.2005.17

[5] S. L. Phung, A. Bouzerdoum, and D. Chai, Skin Segmentation Using Color Pixel Classification: Analysis and Comparison, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, (2005), pp.148-154.

DOI: 10.1109/tpami.2005.17

[6] L. Tao and H.B. Wang, Detecting and Locating Human Eyes in Face Images Based on Progressive Thresholding, Proc. of the 2007 IEEE International Conf. on Robotics and Biomimetics, Sanya, China, Dec. 15-18, (2007), pp.445-449.

DOI: 10.1109/robio.2007.4522203

[7] M. A. Berbar, H. M. Kelash, and A. A. Kandeel, Faces and Facial Features Detection in Color Images, Proc. of the Geometric Modeling and Imaging–New Trends, Jul. 05-06, (2006), pp.209-214.

DOI: 10.1109/gmai.2006.18

[8] Y. Guan, Robust Eye Detection from Facial Image based on Multi-cue Facial Information, Proc. of the 2007 IEEE International Conf. on Control and Automation, Guangzhou, China, May 30- June 1, (2007), pp.1775-1778.

DOI: 10.1109/icca.2007.4376666

[9] ISO/IEC Standard 14496-2, Coding of Audio-Visual Objects: Visual, Oct. (1998).

DOI: 10.3403/03051368

[10] R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, Face Detection in Color Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, (2002), pp.696-706.

DOI: 10.1109/34.1000242