A Mega Super Classifier with Fuzzy Categorization in Face and Facial Expression Identification
Citation
Dr. Goutam Sarker, Dhananjay Bhakta "A Mega Super Classifier with Fuzzy Categorization in Face and Facial Expression Identification", International Journal of Engineering Trends and Technology (IJETT), V51(1),1-11 September 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
A new method for face and facial expression identification from a facial image has been developed through a Mega Super classifier system with fuzzy classification. This system built using two set of super classifier where first super classifier was intended to identify facial expression and second super classifier was designed for person identification. These two decisions are integrated to form the Mega Super classifier output as the person with their expression. This fuzzy classification technique improved the overall system accuracy.
Reference
[1] P. Ekman and W. V. Friesen, ?Facial Acton Coding System, Investigators Guide, Consulting Psychologists Press, 1978
[2] G. Sarker, S. Dhua, M. Besra, ?An Optimal Clustering for Fuzzy Categorization of Cursive Handwritten Text with Weight Learning in Textual Attributes, IEEE 2nd International Conference on Recent Trends in Information Systems (RETIS-2015), pp. 6-11.
[3] D. Bhakta and G. Sarker, ?A Method of Learning Based Boosting in Multiple Classifier for Color Facial Expression Identification, 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), 2015, pp. 319-324
[4] G. Sarker, ?An Unsupervised Natural Clustering with Optimal Conceptual Affinity, Journal of Intelligence System, 2010, 19(3), pp. 289-300
[5] D. Bhakta and G. Sarker, ?An Unsupervised OCA based RBFN for Clear and Occluded Face Identification, First International Conference on Intelligent Computing & Applications 2014 (ICICA 2014), Springer, Advances in Intelligent Systems and Computing, 2014, pp. 19-25
[6] G. Sarker, ?A Heuristic Based Hybrid Clustering for Natural Classification, IJCITAE International Journal of Computers Information Technology and Engineering, 2007, 1(2), pp. 79–86
[7] G. Sarker and S. Sharma, ?A Heuristic Based RBF Network for Location and Rotation Invariant Clear and Occluded Face Identification, International Conference on Advances in Computer Engineering and Application, ICACEA-2014, with IJCA, pp. 30-36
[8] D. Lundqvist, A. Flykt and A. Öhman, ?The Karolinska Directed Emotional Faces – KDEF, CD ROM from Department of Clinical Neuroscience,Psychology section, Karolinska Institutet, ISBN 91-630-7164-9, 1998.
[9] D. Bhakta and G. Sarker, ?A Multiple Classifier System with Learning based Boosting for Clear and Occluded Color Face Identification, American Journal of Advanced Computing, 2014, 1(2), pp. 54-59
[10] Marsico, M., D., Nappi, M., and Riccio, D.; FARO: FAce Recognition Against Occlusion-sand Expression Variations, IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, 40(1), (2010) pp. 121-132.
[11] S. Chintalapati, M. V. Raghunadh, ?Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications, International Journal of Engineering Trends and Technology (IJETT) – Volume 8 Number 6- Feb 2014, pp. 292-298.
[12] Lone, Manzoor Ahmad, S. M. Zakariya, and Rashid Ali. "Automatic face recognition system by combining four individual algorithms." In Computational Intelligence and Communication Networks (CICN), 2011 International Conference on, pp. 222-226. IEEE, 2011.
[13] L. Ma and K. Khorasani, ?Facial Expression Recognition Using Constructive Feed forward Neural Networks, IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, 2004, 34(3), pp. 1588-1595
[14] M. Najafi and M. Jamzad, ?A Solution of Combining Several Classifiers for Face Recognition?, Proceedings of the World Congress on Engineering and Computer Science 2007, WCECS 2007, October 24-26, 2007
[15] H. S. Devi, D. M. Thounaojam and R. Laishram, ?An Approach to Illumination and Expression Invariant Multiple Classifier Face Recognition, International Journal of Computer Applications (0975 – 8887), Volume 91 (15), April 2014, pp. 34-37
[16] M. Pantic and M. J. L. Rothkrantz, ?Facial Action Recognition for Facial Expression Analysis From Static Face Images, IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 2004, 34(3), pp. 1449-1461
[17] Kotsia, I., Buciu, I. and Pitas, I., 2008. An analysis of facial expression recognition under partial facial image occlusion. Image and Vision Computing, 26(7), pp.1052-1067.
[18] J. Kalita and K. Das, ?Recognition of Facial Expression Using Eigenvector Based Distributed Features and Euclidean Distance Based Decision Making Technique, (IJACSA) International Journal of Advanced Computer Science and Applications, 2013, 4(2), pp. 196-202
[19] S. Mahto, Y. Yadav, ?Effectual Approach for Facial Expression Recognition System, International Journal of Advanced Research in Computer and Communication Engineering, Vol 4(3), pp. 229-232, 2015.
[20] Taheri, S., Patel, V.M. and Chellappa, R., 2013. Component-based recognition of facesand facial expressions. IEEE Transactions on Affective Computing, 4(4), pp.360-371.
[21] Hazar Mliki · Emna Fendri · Mohamed Hammami, ?Face Recognition Through Different Facial Expressions?, J Sign Process Syst (2015) 81:433–446, DOI 10.1007/s11265-014-0967-z.
[22] M. A. Martinez, C. A. Kak, ?PCA versus LDA?, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, 23(2), pp. 228-233
[23] H.M. Yang, J. D. Kriegman and N. Ahuja, ?Detecting Faces in Images: A Survey, IEEE Transactions On Pattern Analysis and Machine Intelligence, 2002, 24(1)
[24] M. Hamouz, J. Kittler, K. J. Kamarainen, P. Paalanen, H. l. Ka and J. Matas, ?Feature-Based Affine-Invariant locaization of Faces, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(9), pp. 1490-1495.
[25] Sarker, G.(2000),A Learning Expert System for Image Recognition, Journal of The Institution of Engineers (I), Computer Engineering Division.,Vol. 81, 6-15.
[26] G. Sarker(2002), SELECTWIN – A New K-WTA Optimal Learning Technique for Pattern Classification and Recognition, Journal of The Institution of Engineers(I), Computer Engineering Division.,Vol. 83, 16 – 21.
[27] G. Sarker(2010),A Probabilistic Framework of Face Detection , International Journal of Computer, Information Technology and Engineering (IJCITAE),4(1), 19-25.
[28] G. Sarker(2011),A Multilayer Network for Face Detection and Localization, International Journal of Computer, Information Technology and Engineering (IJCITAE), 5(2), 35-39.
[29] G. Sarker(2012),A Back Propagation Network for Face Identification and Localization, International Journal of Computer, Information Technology and Engineering (IJCITAE),6(1), 1-7.
[30] G. Sarker(2012), An Unsupervised Learning Network for Face Identification and Localization, International Journal of Computer, Information Technology and Engineering (IJCITAE),6(2), 83-89.
[31] G. Sarker and K. Roy (2013), A Modified RBF Network With Optimal Clustering For Face Identification and Localization, International Journal of Advanced Computational Engineering and Networking, ISSN: 2320- 2106.,1(3), 30 -35.
[32] G. Sarker and K. Roy(2013), An RBF Network with Optimal Clustering for Face Identification, Engineering Science International Research Journal (ISSN) – 2300 – 4338 ,1(1),ISBB: 978-93-81583-90-6, 70-74.
[33] G. Sarker(2013), An Optimal Back Propagation Network for Face Identification and Localization, International Journal of Computers and Applications (IJCA),ACTA Press, Canada.,35(2).,DOI 10.2316 / Journal .202.2013.2.202 – 3388.
[34] G. Sarker(2014), A Competitive Learning Network for Face Detection and Localization, International Journal of Computer Information Technology and Engineering (IJCITAE), Serials Publications, 8(2),119-123.
[35] G. Sarker(2002), A Semantic Concept Model Approach for Pattern Classification and recognition, 28th Annual Convention and Exhibition IEEE – ACE 2002.,December 20-21 2002 , Science City ,Kolkata, 271 – 274.
[36] G. Sarker(2005), A Heuristic Based Hybrid Clustering for Natural Classification in Data Mining, 20th Indian Engineering Congress, organized by The Institution of Engineers (India), December 15-18, 2005, Kolkata, INDIA, paper no. 4.
[37] G. Sarker(2011), A Back propagation Network for Face Identification and Localization, 2011 International Conference on Recent Trends in Information Systems (ReTIS–2011) held in Dec. 21-23, Kolkata, DOI: 10.1109/ReTIS.2011.6146834, pp 24-29.
[38] G. Sarker(2012), An Unsupervised Learning Network for Face Identification and Localization, 2012 International Conference on Communications, Devices and Intelligent Systems (CODIS) Dec. 28 and 29, 2012, Kolkata, DOI: 10.1109/CODIS.2012.6422282, pp 652- 655.
[39] G. Sarker, and K. Roy(2013), An RBF Network with Optimal Clustering for Face Identification, International Conference on Information & Engineering Science – 2013(ICIES -2013), Feb. 21-23 2013,
organized by IMRF, Vijayawada, Andhra Pradesh, pp – 70-74.
[40] G. Sarker and K. Roy(2013), A Modified RBF Network with Optimal Clustering for Face Identification and Localization, International Conference on Information & Engineering Science – 2013(ICIES -2013), Feb. 21-23 2013, organized by IMRF, Vijayawada, Andhra Pradesh pp 32-37.
[41] K. Roy and G. Sarker (2013), A Location Invariant Face Identification and Localization with Modified RBF Network, International Conference on Computer and Systems ICCS-2013, 21-22 September, 2013, pp – 23-28, Bardhaman.
[42] D. Bhakta and G. Sarker, ?A Rotation and Location Invariant Face Identification and Localization with or Without Occlusion using Modified RBFN, Proceedings of the 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), 2013, pp. 533-538.
Keywords
Facial Expression Identification, Face Identification, Fuzzy Confusion Matrix, Learning Based Boosting, Mega Super Classifier.