INCT 2011: Innovative Computing Technology pp 258-263 | Cite as
Improving Face Recognition Based on Characteristic Points of Face Using Fuzzy Interface System
Abstract
Three main cases that often are considered for identifying face figures are: happy, sad, and surprised. Face states are created by changes in different points. In this article, first eight characteristic points of face are considered and then five different features are extracted from them that these features form a feature vector for each of the face state. Then, we get a rules database based on these features and with fuzzy inference systems and considering the membership function, a method is presented for identifying happiness and sadness, and surprise states. Three important advantages Compared with other available methods are that it has less number of feature points and features and it has a higher accuracy than other methods.
Keywords
face states detection face characteristic points Fuzzy Inference SystemPreview
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References
- 1.Yann, H., Yang, J., Yang, J.: Bimode model for face recognition and face representation. Int. Journal Neurocomputing (2010)Google Scholar
- 2.Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks 13(6) (2002)Google Scholar
- 3.Sellahewa, H., Jassim, S.A.: Image-Quality-Based Adaptive Face Recognition. IEEE Transactions on Instrumentation and Measurement 59(4) (2010)Google Scholar
- 4.Ahonen, T., Hadid, A., Pietikinen, M.: Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
- 5.Ushida, H., Taking,T., Yamayuchi,T.: Recognition of Facial Expression Using Conceptual Fuzzy Sets. In: IEEE Int. Conf., on Fuzzy Systems, vol. 1 (March 1993)Google Scholar
- 6.Black, M.J., Yacoob, Y.: Recognizing facial Expression in image sequences using local parameterized Model of image motion. Int. Journal of Computer Vision (2003)Google Scholar
- 7.Serdar Yilma, M.A.: Gray Level Based Face Detection Using Template Face Mask and L1 norm. International Journal of Web Applications 2(4), 243–249 (2010)Google Scholar
- 8.Sauvaget, C., Vittaut, J.-N., Suarez, J., Boyer, V., Manuel, S.: Automated Colorization of Segmented Images Based on Color Harmony. Journal of Multimedia Processing and Technologies 1, 228–244 (2010)Google Scholar
- 9.Kosch, H., Maier, P.: Content-Based Image Retrieval Systems - Reviewing and Benchmarking. Journal of Digital Information Management 8(1), 315–331 (2010)Google Scholar
- 10.Wali, A., Alimi, A.M.: Multimodal Approach for Video Surveillance Indexing and Retrieval. Journal of Intelligent Computing 1(4), 165–175 (2010)Google Scholar