Volume-1 , Issue-3 , Jun 2013, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Door Lock System through Face Recognition Using MATLAB

  Ayushi Gupta, Ekta Sharma, Neha Sachan and Neha Tiwari

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.3 , pp.51-55, May-2013

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  Abstract
  Face recognition is a biometric technology with a wide range of potential applications such as access control, banking, information security, human computer interaction, virtual reality, database retrieval etc. This paper addresses the building of face recognition system by using Principal Component Analysis (PCA) method. PCA is a statistical approach used for reducing the number of variables in face recognition. While extracting the most relevant information (feature) contained in the images (face). In PCA, every image in the training set can be represented as a linear combination of weighted eigenvectors called as “Eigenfaces”. These eigenvectors are obtained from covariance matrix of a training image set called as basis function. The weights are found out after selecting a set of most relevant Eigenfaces. Recognition is performed by projecting a new image (test image) onto the subspace spanned by the eigenfaces and then classification is done by distance measure methods such as Euclidean distance. A number of experiments were done to evaluate the performance of the face recognition system.

  Key-Words / Index Term
  PCA, MAX232, MATLab

  References
  [1] Principal Component Analysis for Face Recognition, by Saurabh P.Bahurupi, D.S.Chaudhari, ISSN: 2249 – 8958, Volume-1, Issue-5, June 2012
  [2] Automatic face recognition using principal Component Analysis with DCT by Miss.Renke Pradnya Sunil (Electronics and Telecommunication department, ,Rajarambapu Institute of Technology, India).
  [3] http://www.face-rec.org/algorithms.
  [4] Face Recognition edited by Kresimir Delac and Mislav Grgic,june 2007 edition.
  [5] Face Recognition using Principle Component Analysis, Kyungnam Kim (Department of Computer Science University of Maryland, College Park MD 20742, USA )
  [6] Sukhvinder Singh, Meenakshi Sharma and Dr. N Suresh Rao, “ Accurate Face Recognition Using PCA and LDA”, International Conference on Emerging Trends in Computer and Image Processing (ICETCIP'2011) Bangkok Dec., 2011.
  [7] Hussein Rady, “Face Recognition using Principle Component Analysis with Different Distance Classifiers”(El-Shorouk Academy, Higher Institute for Computer & Information Technology, Egypt), IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.10, October 2011
  

  Citation
  Ayushi Gupta, Ekta Sharma, Neha Sachan and Neha Tiwari, "Door Lock System through Face Recognition Using MATLAB," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.51-55, 2013

• Open Access   Article

  Efficient Computation of Range Aggregates Against Uncertain Location Based Queries using Filtering-and-Verification Algorithm

  S.Praveen Kumar and R.V.Krishnaiah

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.3 , pp.56-61, May-2013

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  Abstract
  In many applications, including location based services, queries may not be precise. In this paper, we study the problem of efficiently computing range aggregates in a multidimensional space when the query location is uncertain. We propose novel, efficient techniques to solve the problem following the filtering-and-verification paradigm.

  Key-Words / Index Term
  Uncertainty, Range Aggregates, Filtering-and-Verification

  References
  [1] P. K. Agarwal, S.-W. Cheng, Y. Tao, and K. Yi. Indexing uncertain data. In Proc. Symp. Principles of Database Systems (PODS), 2009.
  [2] C. Aggarwal and P. Yu. On high dimensional indexing of uncertain data. In Proc. Intl Conf. Data Eng. (ICDE), 2008.
  [3] C. Bohm, M. Gruber, P. Kunath, A. Pryakhin, and M. Schubert. Prover: Probabilistic video retrieval using the gauss-tree. In Proc. Intl Conf. Data Eng. (ICDE), 2007.
  [4] C. Bohm, A. Pryakhin, and M. Schubert. Probabilistic ranking queries on gaussians. In Proc. Intl Conf. Scientific and Statistical Database Management (SSDBM), 2006.
  [5] V. Bryant. Metric Spaces: Iteration and Application. Cambridge University Press, 1996.
  [6] J. Chen and R. Cheng. Efficient evaluation of imprecise location dependent queries. In Proc. Intl Conf. Data Eng. (ICDE), 2007.
  [7] R. Cheng, J. Chen, M. F. Mokbel, and C.-Y. Chow. Probabilistic verifiers: Evaluating constrained nearest-neighbor queries over uncertain data. In Proc. Intl Conf. Data Eng. (ICDE), 2008.
  [8] R. Cheng, D. V. Kalashnikov, and S. Prabhakar. Evaluating probabilistic queries over imprecise data. In Proc. ACM SIGMOD, 2003.
  [9] R. Cheng, S. Singh, and S. Prabhakar. Efficient join processing over uncertain data. In Proc. Int’l Conf. Information and Knowledge Management (CIKM), 2006.
  [10] R. Cheng, Y. Xia, S. Prabhakar, R. Shah, and J. S. Vitter. Effcient indexing methods for probabilistic threshold queries over uncertain data. In Proc. Intl Conf. Very Large Data Bases (VLDB), 2004.
  [11] G. W. Cordner. Police patrol work load studies: A review and critique. Police Studies, 2(3):50–60, 1979.
  [12] X. Dai, M. Yiu, N. Mamoulis, Y. Tao, and M. Vaitis. Probabilistic spatial queries on existentially uncertain data. In Proc. Intl Symp. Large Spatio-Temporal Databases (SSTD), 2005.
  [13] E. Frentzos, K. Gratsias, and Y. Theodoridis. On the effect of location uncertainty in spatial querying. IEEE Trans. Knowl. Data Eng., 21(3):366–383, 2009.
  [14] M. Hua, J. Pei, W. Zhang, and X. Lin. Ranking queries on uncertain data: A probabilistic threshold approach. In Proc. ACM SIGMOD, 2008.
  [15] Y. Ishikawa, Y. Iijima, and J. X. Yu. Spatial range querying for gaussian-based imprecise query objects. In Proc. Intl Conf. Data Eng. (ICDE), 2009.
  [16] H.-P. Kriegel, P. Kunath, M. Pfeifle, and M. Renz. Probabilistic similarity join on uncertain data. In Proc. Intl Conf. Database Systems for Advanced Applications (DASFAA), 2006.
  [17] H. P. Kriegel and M. Pfeifle. Density-based clustering of uncertain data. In Proc. ACM SIGKDD, 2005.
  [18] X. Lian and L. Chen. Monochromatic and bichromatic reverse skyline search over uncertain databases. In Proc. ACM SIGMOD, 2008.
  [19] R. Meester. A Natural Introduction to Probability Theory. Addison Wesley, 2004.
  [20] W. K. Ngai, B. Kao, C. K. Chui, R. Cheng, M. Chau, and K. Y. Yip. Efficient clustering of uncertain data. In Proc. Int’l Conf. on Data Mining (ICDM), 2006.
  [21] J. Pei, B. Jiang, X. Lin, and Y. Yuan. Probabilistic skyline on uncertain data. In Proc. Intl Conf. Very Large Data Bases (VLDB), 2007.
  [22] G. M. Siouris. Missile Guidance and Control Systems. Springer Publication, 2004.
  

  Citation
  S.Praveen Kumar and R.V.Krishnaiah, "Efficient Computation of Range Aggregates Against Uncertain Location Based Queries using Filtering-and-Verification Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.56-61, 2013

• Open Access   Article

  Cross Browser Testing: A Challenge for Web Testing

  Chandraprakash Patidar

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.3 , pp.62-64, May-2013

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  Abstract
  Today’s world is around internet. To access internet we need a browser. Browsers also have variety and versions. For successful browsing we need compatibility among browsers. Web based applications need to test before deploying. A web application is known as successful when it is executed on all the variety and versions of browsers. For this, web applications should be tested on all types of browsers. This type of testing is known as cross browser testing. Cross browser testing is very important issue as different browsers has its own specification and separate architecture. Today’s three most popular browsers are Firefox, Chrome and Internet Explorer. 90% of web testing is performed using these three browsers. Safari also coming close fourth browser. Of course testing only few browsers (Three or Four) is not only the solution of web testing. To cover all web browsers under testing, cross browser testing is very important.

  Key-Words / Index Term
  Browser, Testing, Web application, Versions

  References
  [1] G. Bradski and A. Kaehler. Learning OpenCV. O'Reilly Media, September 2008.
  [2] J. Lewis. Techniques for mobile and responsive cross - browser testing: An envato case study. http://webuild.envato.com 2013.
  [3] A. Mesbah and M. R. Prasad. Automated Cross-browser Compatibility Testing. In Proceeding of the 33rd International Conference on Software Engineering (ICSE), pages 561{570. ACM, May 2011.
  [4] A. Mesbah, A. van Deursen, and S. Lenselink. Crawling Ajax-based Web Applications through Dynamic Analysis of User Interface State Changes. ACM Transactions on the
  Web, 6(1):3:1{3:30, March 2012.
  [5] S. Roy Choudhary, M. R. Prasad, and A. Orso. CrossCheck: Combining Crawling and Di_erencing to Better Detect Cross-browser Incompatibilities in Web Applications. In Proceedings of the IEEE Fifth International Conference on Software Testing, Veri_cation, and Validation (ICST), pages 171{180. IEEE, April 2012.
  [6] S. Roy Choudhary, M. R. Prasad, and A. Orso. X-PERT:
  Accurate Identi_cation of Cross-browser Issues in Web
  Applications. In Proceedings of the 2013 International Conference on Software Engineering, ICSE '13, pages 702{711. IEEE Press, 2013.
  [7] S. Roy Choudhary, H. Versee, and A. Orso. WebDi_:Automated Identi_cation of Cross-browser Issues in WebApplications. In Proceeding of the 2010 IEEE InternationalConference on Software Maintenance (ICSM), pages 1-10.IEEE, September 2010.
  [8] Stackoverow. Posts for cross-browser issues.
  http://data.stackexchange.com May 2014.
  

  Citation
  Chandraprakash Patidar, "Cross Browser Testing: A Challenge for Web Testing," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.62-64, 2013