HYBRID APPROACH FOR HUMAN FACIAL EXPRESSION DETECTION THROUGH TLBO AND PFEF
DOI:
https://doi.org/10.29121/ijetmr.v5.i2.2018.664Keywords:
TLBO, PIFR, PFEF, Feature ExtractionAbstract
This paper introduces facial expression detection method which is based on facial’s selected feature and optimized those selected features. The study says that human face generally faced generally consist of skin color, texture shape and size of face in this paper we study skin color and texture of human face .This process consist two steps for the same. In first known as detection of expression which uses PFEF (partial feature extension function) and in second, for optimization we used TLBO algorithm is basically a population base searching technics. Also uses soft computation technics because we cannot actual and accurate for human related activity. Varieties of technic are used for the same purpose this as per use hybrid approach to get better result.
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Pavel Berkhin, A Survey of Clustering Data Mining Techniques, pp.25-71, 20012. DOI: https://doi.org/10.1007/3-540-28349-8_2
Jain, M. Murty and P. Flynn. Data Clustering: A Review. ACM Computing Surveys, vol. 31, no. 3, pp. 264-323,1999 DOI: https://doi.org/10.1145/331499.331504
Jain, R. Duin and J. Mao. Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.22, no.1, pp. 4-37, 2000. DOI: https://doi.org/10.1109/34.824819
G. Hamerly and C. Elkan. Learning the K in K-means. In The Seventh Annual Conference on Neural Information Processing Systems, 2003.
Jain and R. Dubes. Algorithms for Clustering Data. Prentice Hall, New Jersey, USA, 1988.
S. Baek, B. Jeon, D. Lee and K. Sung. Fast Clustering Algorithm for Vector Quantization. Electronics Letters, vol. 34, no. 2, pp. 151-152, 1998. DOI: https://doi.org/10.1049/el:19980217
Z. Xiang. Color Image Quantization by Minimizing the Maximum Inter-cluster Distance. ACM Transactions on Graphics, vol. 16, no. 3, pp.260-276, 1997. DOI: https://doi.org/10.1145/256157.256159
D. Judd, P. Mckinley and A. Jain. Large-scale Parallel Data Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 871- 876, 1998. DOI: https://doi.org/10.1109/34.709614
Lee and E. Antonsson. Dynamic Partitioned Clustering Using Evolution Strategies. In The Third Asia-Pacific Conference on Simulated Evolution and Learning, 2000.
J. Han , M. Kamber, Data Mining, Morgan Kaufmann Publishers, 2001.
Arun K. Pujari,Data mining techniques-a reference book ,pg. no.-114-147.
K. Jain, “Data Clustering: 50 Years Beyond K-Means, in Pattern Recognition Letters, vol. 31 (8), pp. 651-666, 2010. DOI: https://doi.org/10.1016/j.patrec.2009.09.011
Rama, P. Jayashree, S. Jiwani, “A Survey on clustering Current status and challenging issues”, International Journal of Computer Scienceand Engineering, vol. 2, pp. 2976-2980.
K. Ravichandra Rao, “Data Mining and Clustering Techniques”, DRTC Workshop on Semantic Web, Bangalore, 2003.
Rui Xu, Donald C. Wunsch II, “Survey of Clustering Algorithms”, IEEE Transactions on neural Networks, vol. 16, pp. 645-678, May 2005. DOI: https://doi.org/10.1109/TNN.2005.845141
S.B. Kotsiantis, P. E. Pintelas, “Recent Advances in Clustering: A Brief Survey” WSEAS Transactions on Information Science and Applications, Vol. 1, No. 1, pp. 73–81, Citeseer, 2004.
J. Kelinberg, “An impossibility theorem for clustering”, in NIPS 15, MIT Press, 2002, pp. 446- 453.
B.Amiri, M.Fathian. “Integration of self-organization feature maps and honey bee mating optimization algorithm for market segmentation”,JATIT, Vol. 3, No. 3, Pages 70-86, July, 2007.
T. Kohonen, “Self-Organization and Associative Memory”, Springer-Verlag, New York, vol 10, Page 811-821, 1988. DOI: https://doi.org/10.1007/978-3-662-00784-6
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