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Text mining is the process of extracting information form unstructured to structured text data. The challenging issue in text mining is to extract user required information in efficient manner. To perform this task various data mining methods are used in which the text document analyzed on the basis of term, phrase, concept and pattern. This paper studies the text representation methods and basic term weighing schemes. Ruled-based Phrase Extraction method and Sequential Pattern mining method are discussed to improve the system performance for finding relevant and interesting information.

Text Mining, Text Representaion, Rule based Phrase Extraction, Sequential Pattern Mining

[1] Ning Zhong, Yuefeng Li, and Sheng-Tang Wu, “Effective Pattern Discovery for Text Mining,” IEEE Transactions on Knowledge and Data Engineering, VOL. 24, NO. 1, January 2012.
[2] Y. J. Fu., Data mining: Tasks, techniques and applications, IEEE Potentials, 16(4):18-20, 1997.
[3] Sheng Tang Wu, “Knowledge Discovery using Pattern Taxonomy Model in Text Mining,” Doctor of Philosophy Thesis, Queensland University of Technology, December 2007.
[4] F. Sebastiani. “Machine learning in automated text categorization,” ACM Computing Surveys, 34(1):1–47, 2002.
[5] G. Salton and C. Buckley, “Term-Weighting Approaches in Automatic Text Retrieval,” Information Processing and Management: International Journal, vol. 24, no. 5, pp. 513-523, 1988.
[6] Yutaka Matsuo, Mitsuru Ishizuka “Keyword Extraction from a Single Document using Word Co-occurrence Statistical Information,” FLAIRS 2003.
[7] A. Inkeri Verkamo, Helena Ahonen-Myka, Oskari Heinonen, Mika Klemettinen, “Finding Co-occurring Text Phrases by Combining Sequence and Frequent Set Discovery,” proc. Of the workshop on Text Mining: Foundation, techniques and Applications, IJCAI, 1999.
[8] H. Ahonen, 0. Heinonen, M. Klemettinen, and A. I. Verkamo, “Mining in the phrasal frontier,” In Proceedings of PKDD, pages 343-350, 1997.
[9] H. Ahonen, 0. Heinonen, M. Klemettinen, and A. I. Verkamo “Applying data mining techniques for descriptive phrase extraction in digital document collections,” In Proceedings of the IEEE Forum on Research and Technology Advances in Digital Libraries (ADL98), pages 2-11, 1998.
[10] R. Sharma and S. Raman, “Phrase-Based Text Representation for Managing the Web Document,” Proc. Int’l Conf. Information Technology: Computers and Comm. (ITCC), pp. 165-169, 2003.
[11] P D. Turney, “Learning algorithms for keyphrase extraction,” Information Retrieval, 2(4):303-336, 2000.
[12] Yongzheng Zhang, Nur ZincirHeywood, Evangelos Milios, “Narrative Text Classification for Automatic Key Phrase Extraction in Web Document Corpora,” WIDM ACM 2005.
[13] S. Shehata, F. Karray, and M. Kamel, “Enhancing Text Clustering Using Concept-Based Mining Model,” Proc. IEEE Sixth Int’l Conf. Data Mining (ICDM ’06), pp. 1043-1048, 2006.
[14] S.-T. Wu, Y. Li, and Y. Xu, “Deploying Approaches for Pattern Refinement in Text Mining,” Proc. IEEE Sixth Int’l Conf. Data Mining (ICDM ’06), pp. 1157-1161, 2006.
[15] S. Shehata, F. Karray, and M. Kamel, “A Concept-Based Model for Enhancing Text Categorization,” Proc. 13th Int’l Conf. Knowledge Discovery and Data Mining (KDD ’07), pp. 629-637, 2007.
[16] S.-T. Wu, Y. Li, Y. Xu, B. Pham, and P. Chen, “Automatic Pattern- Taxonomy Extraction for Web Mining,” Proc. IEEE/WIC/ACM Int’l Conf. Web Intelligence (WI ’04), pp. 242-248, 2004.

Vishakha D. Bhope and Sachin N. Deshmukh, "Comparative Study on Information Retrieval Approaches for Text Mining," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.102-106, 2015.

With the increasing use of small and handheld devices, wireless and mobile networks have experienced an unprecedented growth. But their performance significantly depends on the choice of suitable MAC protocol which aims at coordinating access to the shared wireless medium among a number of mobile nodes. Several wireless standards have developed for supporting wireless and mobile networking. Various versions of IEEE 802.11 (Wi-Fi) and IEEE 802.16 (mobile WiMAX) offer physical and MAC layer specifications for WLAN and WMAN respectively. This paper presents a detailed description of the design and technical aspects of MAC 802.11b and MAC 802.16e.

MAC 802.11b (Wi-Fi), MAC 802.16e (mobile WiMAX), MAC layer

With the increasing use of small and handheld devices, wireless and mobile networks have experienced an unprecedented growth. But their performance significantly depends on the choice of suitable MAC protocol which aims at coordinating access to the shared wireless medium among a number of mobile nodes. Several wireless standards have developed for supporting wireless and mobile networking. Various versions of IEEE 802.11 (Wi-Fi) and IEEE 802.16 (mobile WiMAX) offer physical and MAC layer specifications for WLAN and WMAN respectively. This paper presents a detailed description of the design and technical aspects of MAC 802.11b and MAC 802.16e.

Shaili Gaur and Rajnesh Singh, "A Survey on Technical Aspects of MAC 802.11b and MAC 802.16e," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.107-111, 2015.

Advance wireless sensor network (WSN) technology is Low- power electronics and Low-power radio frequency design has enabled the development of small, relatively inexpensive & low-power sensor technology. The important challenges in design of network are three key resource1) Energy 2) Communication bandwidth 3)coverage area . LEACH (Low Energy Adaptive Clustering Hierarchical) is a hierarchical clustering algorithm. It is more efficient than proactive n reactive protocol. LEACH protocol have some disadvantage. To overcome disadvantage we improved LEACH protocol by using optimal path forwarding algorithm and multihop technique i.e O-LEACH protocol. O-LEACH is more efficient than LEACH protocol and it uses static deployment technique.

LEACH protocol, Wireless Sensor Network , Energy Efficient

[1] Heinzelman.W.B.,Chandrakasan.A.P., Balakrishnan.H“An application-specific protocol architecture for wireless microsensor networks” IEEE transactions on Wireless Communication, Vol. 1, Issue. 4, 2002, pp 660-670
[2] Yuhua Liu, Yongfeng Zhao, JingjuGao, “A New Clustering mechanism based on LEACH Protocol”, 2009 International Joint Conference on Artificial Intelligence, 2009. JCAI '09. pp 715-718
[3] Beibei Wang & Chong Shen & Jing Li “ Study and Improvement on LEACH protocol in WSN’S” International Journal of Wireless Communication and Networking ISSN :0975-7163
[4] V.Loscrì ,G. Morabito, S. Marano,“A two levels hierarchy for low energy adaptive clustering hierarchy (TL-LEACH)”, Vehicular Technology Conference, 2005, Vol. 3, pp 1809-1813
[5] Haosong Gou and YounghwanYoo“An Energy Balancing LEACH Algorithm for Wireless Sensor Networks “978-0-7695-3984-3/10 $26.00 © 2010 IEEE
[6] Jlan-FengYann& Yuan-Liu Liu “Improved LEACH Routing Protocol ForLarge Scale Wireless Sensor Networks Routing” 978-1-4577-0321-8/11/$26.00 ©2011 IEEE
[7] Ms.V.MuthuLakshmi “Advanced LEACH Protocol in Large scale WirelessSensor Networks” Volume 4, Issue 5, May-2013 ISSN 2229-5518
[8] Nihal Srivastava, Sandeep Seth, Mohit Kumar, Rakesh Kumar “ Energy Efficient Protocols For Wireless Sensor Networks: A Review “Int.J.Computer Technology & Applications,Vol 4 (3),434-444 IJCTA | May-June 2013
[9]Pooja Singh , Vikas Pareek, Anil K Ahlawat, “ Performance Comparison Of Energy Efficient Protocols For Wireless Sensor Networks (WSN) “ International Journal Of Computer Applications (0975 – 8887) Volume 90 – No 4, March 2014
[10] Parth M Dave1, Purvang D Dalal “Simulation & Performance Evaluation Of Routing
Protocols In Wireless Sensor Network” International Journal Of Advanced Research In Computer And Communication Engineering Vol. 2, Issue 3, March 2013

Priyanka khralkar, "A Survey on Clustering Protocol LEACH using Static Deployment Technique for WSN," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.112-115, 2015.

Today’s digital word technologies information in computer world, there is extremely large increase in crime like money laundering, unauthorized access, ethical hacking, fraud detection in different domain etc. So, investigation of such cases deserve a lot more important, in this forensic investigation computer devices plays a major role. In Digital forensic analysis seized digital devices can provide precious information and evidences about facts and/or individuals on which the investigational activity is performed. In this paper we proposed document clustering algorithms to digital forensic analysis of computers seized devices in police investigations. In the Digital forensic analysis of investigation, used total six famous partition (k-means, k-medoids and CSPA) algorithm and hierarchical (Single Link, Complete Link and Average Link) document clustering algorithms are used. This is applied datasets for five real-world investigation cases conducted by the Brazilian Federal Police Department. Also two validity index are used to find out how many clusters are formed. This experiments show that the Average Link and Complete Link algorithms provide the best results for the application domain. This reviews different existing text clustering and Document clustering multithreading methods is used with computer forensic analysis.

Digital Forensic Analysis, Document Clustering, Text Clustering,Multithreading

[1] Filipe daCruz, Nassif and Eduardo Raul Hruschka, “Document Clustering for Forensic Analysis: An Approach for Improving Computer Inspection” IEEE, Transactions on information forensics and security, VOL 8.No. 1, January 2013.
[2] A. K. Jain, R. C. Dupes, Algorithms for Clustering Data. Englewood Cliffs, NJ: Prentice-Hall, 1988.
[3] L. Kaufman, P. Rousseeuw, Finding Groups in Gata: An Introduction to Cluster Analysis. Hoboken, NJ: Wiley-Interscience, 1990.
[4] R. Xu, D. C.Wunsch, II, Clustering. Hoboken, NJ: Wiley/IEEE Press, 2009.
[5] A. Strehl and J. Ghosh, “Cluster ensembles: A knowledge reuse framework for combining multiple partitions,” J. Mach. Learning Res., vol. 3, pp. 583–617, 2002.
[6] B. K. L. Fei, J. H. P. Eloff, H. S. Venter, and. S. Oliver, “Exploring forensic data with self-organizing maps,” in Proc.IFIP Int. Conf. Digital Forensics, 2005, pp. 113–123.
[7] N. L. Beebe and J. G. Clark, “Digital forensic text string searching: Improving information retrieval effectiveness by thematically clustering search results,” Digital Investigation, Elsevier, vol. 4, no. 1, pp. 49–54, 2007.
[8] F. Iqbal, H. Binsalleeh, B. C. M. Fung, and M. Debbabi, “Mining write prints from anonymous e-mails for forensic investigation,” Digital Investigation, Elsevier, vol. 7, no. 1–2, pp. 56–64, 2010.
[9] R. Hadjidj, M. Debbabi, H. Lounis, F. Iqbal, A. Szporer, and D. Benredjem, “Towards an integrated e-mail forensic analysis framework, “Digital Investigation, Elsevier, vol. 5, no. 3–4, pp. 124–137, 2009.
[10] L. Liu, J. Kang, J. Yu, and Z. Wang, “A comparative study on unsupervised feature selection methods for text clustering,” in Proc. IEEE Int. Conf. Natural Language Processing and Knowledge Engineering, 2005, pp. 597–601.
[11] L. Vendramin, R. J. G. B. Campello, and E. R. Hruschka, “Relative clustering validity criteria: A comparative overview,” Statist. Anal. Data Mining, vol. 3, pp. 209–235, 2010.
[12] Jensen, J.H., Ellis, D., Christensen, M.G., Jensen, and S.H.: Evaluation distance measures between Gaussian mixture models of mfccs. Proc. Int. Conf. on Music Info. Retrieval ISMIR-07 Vienna, Austria pp. 107–108 (October, 2007)
[13] C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2006.

Vilas V Pichad and Sachin N Deshmukh, "Role of Document Clustering For Forensic Analysis Investigation System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.116-120, 2015.

Due to the insecure environment the ratio of vehicle theft increases rapidly. Because of this is manufacturers of luxury automobiles has the responsibilities for taking steps to ensure the authorization for the owners and also in built the anti theft system to prevent the vehicle from theft. The proposed security system for smart cars used to prevent them from loss or theft using FIM 5360 and PIC16F877A processor. It performs the real time user authentication using face recognition and finger print detection. According to the comparison result, processors trigger certain actions. If the result is not authentic means processors produces the signal to block the car access. It produce the interrupt signal to car engine to stop its action, alarm and inform the car owner about the unauthorized access via SMS and sends image to owners mailbox with the help of GSM modem.

FIM 5360 processor,GSM module,finger print recognition,face detection

[1] Jian Xiao and HaidongFeng “A Low-cost Extendable
Framework for Embedded Smart Car Security System”
Proceedings of the 2009 IEEE International Conference
on Networking, Sensing and Control, Okayama,
Japan,March 26-29, 2009.
[2]Vinoth Kumar Sadagopan, UpendranRajendran, Albert
Joe Francis”Anti-Theft Control System Design Using
Embedded System” Proceedings of the 2011 IEEE
International Conference.
[3]S.Padmapriya, EstherAnnlinKalaJames“Real Time Smart
Car Lock Security System Using Face Detection and
Recognition” 2012 International Conference on Computer
Communication and Informatics ( ICCCI -2012), Jan. 10
– 12, 2012, Coimbatore, INDIA.
[4]Vivek Kumar Sehgal, Sudeep Singh ShivangiKulshrestha,
MuditSinghal, BhartMangla“An Embedded Interface for
GSM Based Car Security System ”2012 Fourth
International Conference on Computational
Intelligence, Communication Systems and Networks.
[5]Manjunath T K, Andrews Samraj, N
Maheswari,SharmilaChidaravalli “Locking and
Unlocking of Theft Vehicles Using CAN (Theft Control
System)”Proceedings of 2013 International Conference
on Green High Performance Computing March 14-
15,2013, India

Alex N.V, Filma Mathew, Sini Jacob, Vaneza Benny and Bineesh M, "Car Security - Vehicle Theft Identity and Control System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.121-124, 2015.

In this paper we are trying to explain about firewall. Firewall is required to secure your valuable information. As there are many ways to deal with network security, we are trying to use FSA for providing a security because it is powerful enough to express any possible stateless packet filter and also provide optimal result in case of multiple packet filter combined together. As filtering is somewhat difficult with stateless packets we are trying to solve some issues related to stateless packet filter.

Firewall, packet filtering, Stateful firewall, stateless firewall,FSA, WinPcap security policy

[1]Meng-meng Zhang, Yan Sunand Jingzhong Wang,”A Fast Regular Expressions Matching Algorithm for NIDS”, Applied Mathematics & Information SciencesanInternational Journal Mar. 2013
[2]Shubhash Wasti, Department of Computer Science, University of Saskatchewan, “Hardware Assisted Packet Filtering Firewall”.
[3] J. C. Mogul, R. F. Rashid, and M. J. Accetta, “The packet filter: An efficient mechanism for user-level network code,” in Proc. 11th ACM Symp. Oper. Syst. Principles, Austin, TX, USA, Nov. 1987, pp. 39–51.
[4]M. L. Bayley, B. Gopal, M. A. Pagels, and L. L. Peterson, “PATHFINDER: A pattern-based packet classifier,” in Proc. 1stUSENIX Symp. Oper. Syst. Design Implement., Monterey, CA, USA,Nov. 1994, pp. 115–123.
[5] A. Begel, S. McCanne, and S. L. Graham, “BPF+: Exploiting global data-flow optimization in a generalized packet filter architecture,” Comput. Commun. Rev., vol. 29, no. 4, pp. 123–134, Oct. 1999.
[6] D. R. Engler and M. F. Kaashoek, “DPF: Fast, flexible message demultiplexing using dynamic code generation,” in Proc. ACM SIGCOMM, Stanford, CA, USA, Aug. 1996, pp. 53–59.
[7] Z. Wu, M. Xie, and H. Wang, “Swift: A fast dynamic packet filter,” in Proc. 5th USENIX Symp. Netw. Syst. Design Implement., San Francisco, CA, USA, Apr. 2008, pp. 279–292.
[8]Zouheir Trabelsi, UAE University, “Teaching Stateless And Statefull Firewall Packet Filtering: A Hands On Approach”, 16th Colloquium for Information Systems Security Education Lake Buena Vista, Florida June 11 - 13, 2012.
[9] Navneet Kaur Dhillon and Mrs. Uzma Ansari,”Enterprise Network Traffic Monitoring,analysis and reporting using WINPCAP tool with JPCAP API”,ijarcsse,Volume 2, Issue 11,November 2012.
[10]C. Jasmine, Dr. T. Latha,”Finite Automata in Pattern matching for Hardware based NIDS Applications – a Tutorial and Survey”,Progress In Science in Engineering Research Journal,PISER 12, Vol.02, Issue: 02/06 March- April; Bimonthly International Journal Page(s) 351-360

Kakkad Kruti M and Prof. Krunal Vaghela, "Modeling Multiple Packet Filter With FSA For Filtering Malicious Packet," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.125-128, 2015.

Internet plays a crucial role in our daily life, from shopping to banking one can do anything just by a single click. Similarly, seeking jobs through online portals has become much convenient for the candidates as well as the recruiters. These portals help recruiter to find the perfect candidate and the candidate to get their perfect job. Automated and user-friendly software is being created which endows with a solution to parse all the resumes and provides a quality candidates required for the job. This paper deals with the parsing application developed for the resumes received in multiple formats like doc, docx, pdf, txt. When a company has a vacant position, it receives thousands of resumes for a single position. The concerned authorities have to go through each parameter of the resume and then select the candidates, for the interview. However, this system transforms original resumes into a standard format which contains only desired details of the candidate.

Natural Language Processing, Metadata, Parsing, Standardization, Recruiter, Candidate

[1] C# coding,
http://www.c-sharpcorner.com/beginners/
last visited :February 2015.
[2] Christian Nagel, Jay Glynn, “C# 5.0 and .NET 4.5.1”, Wiley, Page No: 24-63, 210-228, 415-457.
[3] Sovren Résumé / CV Parser, http://www.sovren.com/
last visited: November 2014.
[4] Daxtra CVX:
,http://www.daxtra.com/,
last visited: November 12, 2014.
[5] Zhang Chuang, Wu Ming, Li Chun Guang, Xiao bo, Lin Zhi-qing, “Resume Parser: Semi-structured Chinese document analysis”, Computer Science and Information Engineering, 2009 WRI World Congress on, Volume-5, Page No(12-16), 2009.
[6] Creating Web forms in visual studio using asp.net, https://www.youtube.com/watch?v=wU6roQF9uUE , last visited: January 2015.
[7] Learning the basics of asp.net, http://www.w3schools.com/aspnet/ , last visited : October 2014.
[8] Existing Portals , Naukri.com, http://www.naukri.com, last visited: February 2015.
[9] Jigyasa Nigam and Sandeep Sahu, “Fast and Effective System for Name Entity Recognition on Big Data”, Int. Journal of Computer Sciences and Engineering, Volume-3, Issue-2, Page No(31-35), Jan 2014
[10] Sneha Lohbare and Ashwini Meshram, “A Review on Document RetrivalRetrival From Unstructured Text”, Int. Journal of Computer Sciences and Engineering, Volume-2, Issue-11, Page No(76-80), Nov 2014

Pooja Shivratri1, Preeti Kshirsagar, Rashmi Mishra Ronit Damania and Nandana Prabhu, "Resume Parsing and Standardization," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.129-131, 2015.

This paper presents a computer vision-based approach for automatically detecting the presence of fire and smoke in video sequences. Since the fire causes serious disasters, fire detection has been an important study to protect human life. In this paper, the system proposed the fire- smoke detection algorithm in video sequence. The system focuses on optimizing the flame detection by identifying gray cycle pixels nearby the flame, which is generated because of smoke and of spreading of fire pixel and the area spread of flame. The model uses different color model for both fire and smoke and also the model use fuzzy logic or fuzzy inference system (FIS) to detect fire pixels. These techniques can be used to reduce false alarm by giving the accurate result of fire occurrence along with fire detection methods. The color models are extracted using statistical analysis of samples extracted from different type of video sequence and images. The extracted models can be used in complete fire/smoke detection system which combines color information with motion analysis. The strength of using video in flame detection is the ability to monitor large and open spaces. The system also give the opportunity to adjust the system by applying different combination of fire detecting techniques which will help in implementation of system according to different sensitive area requirement.

Fire detection, Smoke detection video precessing, Fuzzy Inference System (FIS), Mamdani Model, RGB, YCbCr

[1] TurgayÇelik, HüseyinÖzkaramanlı and HasanDemirel, “Fire and Smoke Detection without Sensors: Image Processing Based Approach”, 15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September-3-7, 2007.
[2] GauravYadav,Vikas Gupta, Vinod Gaur and Dr. MahuaBhattacharya, “Optimized Flame Detection Using Image Processing Based Techniques”, ISSN: 0976-5166, Vol. 3 No. 2, April-May, 2012.
[3] VipinVenu, “Image Processing Based Forest Fire Detection”, ISSN 2250-2459, Volume 2, Issue 2, February 2012.
[4] Surya T.S, Suchithra M.S, P.G. Student, “Survey on Different Smoke Detection Techniques Using Image Processing”, ISSN (O): 2278-5841, 2014.
[5] Chen, T., Wu, P., Chiou, Y., “An early fire-detection method based on image processing”, Proc. IEEE Internat. Conf. on Image Processing, ICIP’04, pp. 1707-1710, 2004.
[6] Klir, G. J., Yuan B., “Fuzzy Sets and Fuzzy Logic”, Prentice Hall, 1995.
[7] Mathews, J.H., Fink, K.D., “Numerical Methods using matlab”, Prentice Hall, 1999.
[8] Turgay Celik, Huseyin Ozkaramanli, Hasan Demirel, “Fire Pixel Classification Using Fuzzy Logic and Statistical Color Model”, ICASSP 2007.
[9] T. Chen, P. Wu, and Y. Chiou (2004): “An early fire-detection method based on image processing”, in ICIP ’04, pp.1707–1710.

[10] C.-B. Liu, N. Ahuja (2004): “Vision based fire detection”, Proceedings of the 17th International Conference on Pattern Recognition (ICPR’ 04), Vol.4, pp. 134-137.
[11] S. Noda, K. Ueda (1994): “Fire detection in tunnels using an image processing method”, in Vehicle Navigation & Information Systems Conference Proceedings, pp. 57-62.
[12] TurgayCelik (2010): “Fast and Efficient Method for Fire Detection Using Image Processing”, ETRI Journal, Volume 32, Number 6.

Meena Ugale, Abhilash Nunes, Leroy Dias and Shalem Pereira , "Smoke and Fire Detection in Videos," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.132-144, 2015.

This project is based on the design and development of Remote Surveillance System. Services of this system are useable for clients with not only PC’s but also mobile devices which has internet connectivity (GPRS or Wi-Fi). This “Remote surveillance System” is a Hardware and software Integrated product. In traditional CCTV cameras the recorded video is stored on a DVR kept somewhere in the premises where the CCTV is installed .Since there is a physical access to the stored video, smart robbers can steal it during act of mishap. So there is need of comprehensive solution for the problem of breaching of Traditional security systems. Hence introducing “Remote Surveillance System” with Android software, Cloud service and Camera Hardware is the desired solution. The storage of the recorded video will be done online. The recorded video can be seen by the user to find out any suspicious activities happened in surveillance area with the help of android software from any remote location and also the live streaming of surveillance area.

Survelliance, Android, Cloud, Integrated product, Live streaming.

[1] TasleemMandrupkar,ManishaKumari,Rupali Mane, “Smart Video Security Surveillance with Mobile Remote Control”, International Journal of Advanced Research in Computer Science and Software Engineering, ISSN: 2277 128X , Volume 3, Issue 3, March 2013
[2] SonaliDiware ,ShwetaIskande ,” Remote Surveillance System for Mobile Application”, The International Institute for Science, Technology and Education (IISTE), ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online)Vol 3, No.5, 2012
[3] Wei Chen,Chien-Chou Shih,Lain-Jinn Hwang, ”The Development and application of the Remote real-time Video Surveillance System”, Tamkang Journal of Science and Engineering,Vol.13,No.2,pp.215-225(2010)
[4] CCTV and Video Analytics initiatives in transit operations, Alcatel-Lucent Paris Forum, March 2009,DaveGorshkov.
[5] Building Digital Security & Surveillance Systems Based on Intel Technology,Intel

Savita Lohiya, Hrishikesh Bansode, Shivkumar Nadar, Sangam Menon , "Remote Surveillance System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.145-147, 2015.

Identifying faces in images is easier but face identification in videos is more difficult than that in images because of low resolution, occlusion, non-rigid deformations, large motion, complex background and other uncontrolled conditions make the results of face detection and recognition unreliable. It is a challenging problem due to the huge variation in the appearance of faces in video to achieve accuracy. The main objective of proposed system is to efficiently identify faces even in case of occlusion like glasses, etc. which results in accuracy of system. Facial occlusions, due for example to sunglasses, hats, scarf, beards etc., can significantly affect the performance of any face recognition system. Unfortunately, the presence of facial occlusions is quite common in real-world applications especially when the individuals are not cooperative with the system such as in video surveillance scenarios. While there has been an enormous amount of research on face recognition under pose/illumination changes and image degradations, problems caused by occlusions are mostly overlooked. The focus of this paper is thus on facial occlusions, and particularly on how to improve the recognition of faces occluded by sunglasses and scarf. We propose an efficient approach which demonstrates state-of-the-art performance on streaming video face recognizing in various genres of videos and label them with the corresponding relevant names.

Face Detection, Face Recognition, Facial Occlusion, Streaming Video

[ 1] Jeong-Seon Park, You Hwa Oh, Sang Chul Ahn , and Seong-Whan Lee, Sr. Memb “Glasses Removal from Facial Image Using Recursive Error Compensation”. IEEE Transaction on Pattern Analysis And Machine Intelligence.Vol. No.27 2012
[2] Maria De Marsico, Member, IEEE, Michele Nappi, and Daniel Riccio “Face Recognition Against Occlusions and Expression Variations . IEEE Transaction on System , Man, And Cybernetics And Humans, VOL. 40, NO. 1, January 2010
[3] Zafar G. Sheikh, V. M. Thakare, S. S. Sherekar “Advances in Face Detection Techniques in Video”2012 2nd IEEE International Conference on Parallel, Distributed and Grid Computing.
[4] Minglan Sheng, Zhangli Lan “Face Detection in Video Sequence with Complex Background” Proceedings of 2008 IEEE International Conference on electronics and Automation.
[5] Rabia Jafri and Hamid R. Arabnia* “A Survey of Face Recognition Techniques” International Journal of Information Processing Systems, June 2009
[6] Face Detection System on Ada boost Algorithm Using Haar Classifiers M. Gopi Krishna,A. Srinivasulu, Prof (Dr.) T.K.Basak International Journal of Modern Engineering Research (IJMER) Nov-Dec. 2012
[7] “Improving the Recognition of Faces Occluded by Facial Accessories “ Rui Min Multimedia Communications Dept. Abdenour Hadid Machine Vision Group from University of Oulu, Finland.
[8] B.G. Park, K.M. Lee, and S.U. Lee, “Face recognition using face-ARG matching,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 12, pp. 2005.
[9]Ali Javed ,Face Recognition Based on Principal Component Analysis J. Image, Graphics and Signal Processing, Feb 2013 .
[10] Ole Helvig Jensen Kongens Lyngby “Implementing the Viola-Jones Face Detection Algorithm”. Kongens Lyngby 2008 in Proc. ICME, 2011, pp. 1–6.

[11] Zafar G. Sheikh, V. M. Thakare, S. S. Sherekar 2nd IEEE International Conference on Parallel, Distributed and Grid Computing “Advances in Face Detection Techniques inVideo”.
[12] W. Zhang, S. Shan, X. Chen, and W Gao, "Local Gabor Binary Patterns Based on Kullback–Leibler Divergence for Partially Occluded Face Recognition," Signal Processing Letters, IEEE , vol.14, no.11, pp.875- 878, Nov. 2007.
[13]P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," Proc. Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
[14]J. Yang and A.Waibel. A real-time face tracker. In Proceedings of the Third IEEE Workshop on Applications of Computer Vision, pages 142-147, Sarasota, FL, 1996.
[15]Mikolajczyk, K.; Choudhury, R.; Schmid, C. ,Face detection in a video sequence-a temporal approach, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol 2, pp: 96-101, 2001.
[16] Zhenqiu Zhang; Potamianos, G.; Ming Liu; Huang, T., Robust Multi-View Multi-Camera Face Detection inside Smart Rooms Using Spatio-Temporal Dynamic Programming, 7th International Conference
on Automatic.
[17] G. Bradski. Computer vision face tracking for use in a perceptual user interface. Technical Report Q2, Intel Corporation, Microcomputer Research Lab, Santa Clara, CA, 1998.
[18] H. Schneiderman and T. Kanade. "A statistical method for 3d object detection applied to faces and cars". In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2000.
[19] J. Feraud, O. Bernier, and M. collobert. "A fast and accurate face detector for indexation of face images". In Proc. Fourth IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 52-59,
1998.
[20] S. Gong, S.McKenna, and J.Collins. "An investigation into face pose distribution". In Proc. IEEE International Conference on Face and Gesture Recognition, Vermont,1996.
[21] ZhenQiu Zhang; Long Zhu; Li, S.Z.; HongJiang Zhang,Real-time multi-view face detection, Fifth IEEE International Conference on Automatic Face and Gesture Recognition, pp:142-147,2002.
[22] P. Viola and M.J. Jones, "Robust real-time object detection", IEEE ICCV Workshop on Statistical and Computational Theories of Vision. Vancouver, Canada. July 13, 2001.
[23] S. Z. Li, Z. Q. Zhang, "FloatBoost Learning and Statistical Face Detection", IEEE Transactions on Pattern
Analysis and Machine Intelligence, VOL. 26, NO. 9, September, 2004.
[24] Yan Wang; Yanghua Liu; Linmi Tao; Guangyou Xu ,Real-time multi-view face detection and pose estimation in video stream, 18th International Conference on Pattern Recognition ,pp: 354 - 357,2006.
[25] M. Nakamura, H. Nomiya and K. Uehara, "Improvement of boosting algorithm by modifying the weighting rule", Annals of Mathematics and Artificial Intelligence, 41:95-109,2004.
[26] C.J. Edward, C.J. Taylor, and T.F. Cootes, "Learning to Identify and Track Faces in an Image Sequence," Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 260-265, 1998.
[27]J. Yang and A.Waibel. A real-time face tracker. In Proceedings of the Third IEEE Workshop on Applications of Computer Vision, pages 142-147, Sarasota, FL, 1996.

Swati Kamble and R. K. Krishna , "A Review: Video Face Recognition under Occlusion," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.148-155, 2015.