iosr-JCE   Volume-1 ~ Issue-2

ABSTRACT : This paper present an approach to recognize human face expression and emotions based on some fuzzy pattern rules. Facial features for this specially eye and lips are extracted an approximated into curves which represents the relationship between the motion of features and change of expression. This paper focuses the concepts like face detections, skin color segmentation, face features extractions and approximation and fuzzy rules formation. Conclusion based on fuzzy patterns never been accurate but still our intension is to put more accurate results.
Keywords -Face Detection, Skin Color Segmentation, Face Futures, Curve Formation and Approximation, Fuzzy Patterns.

[1] Y. Yacoob and L.S. Davis, "Recognizing human facial expressions from long image sequences using optical flow", IEEE Trans. Pattern Analysis & Machine Intelligence, Vol. 18, No 6, pp. 636-642, 1996.
[2] P. Ekman and W. Friesen, "Facial Action Coding System", Consulting Psychologists Press, 1977.
[3] K. Aizawa and T. S. Huang, "Model-based image coding: Advanced video coding techniques for very low bit-rate applications", Proc. IEEE, Vol. 83, No. 2, pp. 259-271, 1995.
[4] S. Kimura and M. Yachida, "Facial expression recognition and its degree estimation", Proc. Computer Vision and Pattern Recognition, pp. 295-300, 1997.
[5] K. Ohba, G. Clary, T. Tsukada, T. Kotoku, and K. Tanie, "Facial expression communication with FES", Proc. International Conference on Pattern Recognition, pp. 1376-1378, 1998.
[6] M.A. Bhuiyan and H. Hama, "Identification of Actors Drawn in Ukiyoe Pictures", Pattern Recognition, Vol. 35, No. 1, pp. 93-102, 2002.
[7] M. B. Hmid and Y.B. Jemaa, Fuzzy Classification, Image Segmentation and Shape Analysis for Human Face Detection. Proc. Of ICSP, vol. 4, 2006.
[8] M. Wang, Y. Iwai, M. Yachida, "Expression Recognition from Time-Sequential Facial Images by use of Expression Change Model", Proc. Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 324 – 329, 1998.
[9] M. I. Khan and M. A. Bhuiyan, "Facial Expression recognition for Human-Machine Interface", ICCIT, 2006.

ABSTRACT: Object tracking is an important task within the field of computer vision. It is a challenging problem. Many difficulties arises in tracking the objects due to abrupt object motion, changing appearance patterns of both the object and the scene, non-rigid object structures, object-to-object and object-to-scene occlusions, and camera motion. This paper selectively reviews the research papers with regard to tracking methods on the basis of the object, their motion representations and all detailed descriptions of representative methods in each category examining their advantages/disadvantages. It also discusses the important issues related to tracking including the use of object representation, tracking, and detection.
Keywords – Object Representation, Object Tracking, Object Detection, Computer Vision.

[1] Pengwei LIU, Huiyuan WANG et al., Motion Compensation Based Detecting and Tracking Targets in Dynamic Scene, IEEE, 2010, 703-706.
[2] Sajjad Torkan, Alireza Behrad, A New Contour Based Tracking Algorithm Using Improved Greedy Snake, IEEE, 2010.
[3] Baiyang Liu, Lin Yang et al., An Adaptive Tracking Algorithm Of Lung Tumors In Fluoroscopy Using Online Learned Collaborative Trackers, IEEE, 2010, 209-212.
[4] Alexander Toshev, Ameesh Makadia, Kostas Daniilidis, Shape-based Object Recognition in Videos Using 3D Synthetic Object Models, IEEE, 2009, 288-295.
[5] Ming-Yu Shih, Yao-Jen Chang, Bwo-Chau Fu, and Ching-Chun Huang, Motion-based Background Modeling for Moving Object Detection on Moving Platforms, IEEE, 2007, 1178-1182.
[6] Mark Ritch, Nishan Canagarajah, Motion-Based Video Object Tracking In The Compressed Domain, IEEE, 2007, 301-304.
[7] Alper Yilmaz, Omar Javed, Mubarak Shah, Object Tracking:A Survey, ACM Computing Surveys, 38(4), 2006.
[8] Rajan Sehgal, Video Image Enhancement and Object Tracking, A Thesis, Thapar Institute of Engineering and Technology, Patiala, ME, 2006.
[9] Minglun Gong, A GPU-based Algorithm for Estimating 3D Geometry and Motion in Near Real-time, IEEE, 2006.
[10] Huiqiong Chen, Derek Rivait and Qigang Gao, Real-Time License Plate Identification by Perceptual Shape Grouping and Tracking, IEEE, 2006, 1352-1357.

ABSTRACT:Opinion retrieval has established itself as an important part of search engines ratings, opinion trends and representative opinions enrich the search experience ofusers when combined with traditional document retrieval by revealing more insights about a subject.In the past years we have witnessed Sentiment Analysis and OpinionMining becoming increasingly popular topics in InformationRetrieval and Web data analysis.With the rapid growth of the user-generated content on the Web.Opinion retrieval is a document retrieving and ranking process, a relevant document must be relevant to the query and contain opinions toward the query. Opinion polarity classification is an extension of opinion retrieval; it classifies the retrieved document as positive, negative or mixed, according to the overall polarity of the query relevant opinions in the document. In this study, we review the development of opinion search and retrieval during the last years, and also discuss the evolution of a relatively newresearch directionand we try to layout the futureresearch directions in the field.
Keywords:Opinion mining, Opinion Retrieval, Opinion Identification, Text Mining,

[1] Ana-Maria Popescu and Oren Etzioni.Extractingproduct features and opinions from reviews..In Proceedings of Human LanguageTechnology Conference and Conference on Empirical Methods in Natural Lan-guage(HLT/EMNLP),2005, 339-346.
[2] G. Amati. Probabilistic models for informationretrieval based on Divergence from Randomness.PhDthesis, University of Glasgow, 2003.
[3] Bing Liu,"sentimentanalysi and subjectivity" to appear in Handbook of Natural Language Processing,Second Edition.(editors: N. Indurkhya and F.J. Damerau),2010
[4] BenHe&JiyinHeIadhOunis"An Effective Statistical Approach to Blog Post OpinionRetrieval"CIKM'08, October 26–30, 2008, Napa Valley, California, USA.
[5] Bo Pang and Lillian Lee. A sentiment education: Senti-ment analysis using subjectivity summarization based on minimum cuts. InProceedings of the 42nd Annual Meeting of the Association for ComputationalLinguistics(ACL)2004. 271-278.
[6] Binali,H.,Potdar, V.1 and Chen wul.(2009).A state of the art opinion Mining and Its application domains. IEEE International Conference on Industrial Technology.01/01/2009.
[7] A. Esuli and F. Sebastiani, "Determining the semantic orientation of terms through glossanalysis," Proceedings of the ACM Conference on Information and Knowledge Management(CIKM), 2005.
[8] A. Esuli and F. Sebastiani, "Determining term subjectivity and term orientation for Opinionmining,"Proceedings of the European Chapter of the Association for Computational Linguistics(EACL), 2006
[9] A. Esuli and F. Sebastiani, "SentiWordNet: A publicly available lexical resource for pinionmining," Proceedings of Language Resources and Evaluation(LREC), 2006.
[10] A. Esuli and F. Sebastiani, "PageRankingWordNetsynsets: An application to opinion mining,"