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OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
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OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD
CELL: +91 9894917187 | ...
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Advanced joint bayesian method for face verification

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Final Year IEEE Projects for BE, B.Tech, ME, M.Tech,M.Sc, MCA & Diploma Students latest Java, .Net, Matlab, NS2, Android, Embedded,Mechanical, Robtics, VLSI, Power Electronics, IEEE projects are given absolutely complete working product and document providing with real time Software & Embedded training......

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Networking, Network Security, Data Mining, Cloud Computing, Grid Computing, Web Services, Mobile Computing, Software Engineering, Image Processing, E-Commerce, Games App, Multimedia, etc.,

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Embedded Systems,Micro Controllers, DSC & DSP, VLSI Design, Biometrics, RFID, Finger Print, Smart Cards, IRIS, Bar Code, Bluetooth, Zigbee, GPS, Voice Control, Remote System, Power Electronics, etc.,

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Android Application, Web Services, Wireless Application, Bluetooth Application, WiFi Application, Mobile Security, Multimedia Projects, Multi Media, E-Commerce, Games Application, etc.,

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Auto Mobiles, Hydraulics, Robotics, Air Assisted Exhaust Breaking System, Automatic Trolley for Material Handling System in Industry, Hydraulics And Pneumatics, CAD/CAM/CAE Projects, Special Purpose Hydraulics And Pneumatics, CATIA, ANSYS, 3D Model Animations, etc.,

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Advanced joint bayesian method for face verification

  1. 1. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ADVANCED JOINT BAYESIAN METHOD FOR FACE VERIFICATION By A PROJECT REPORT Submitted to the Department of electronics &communication Engineering in the FACULTY OF ENGINEERING & TECHNOLOGY In partial fulfillment of the requirements for the award of the degree Of MASTER OF TECHNOLOGY IN ELECTRONICS &COMMUNICATION ENGINEERING APRIL 2016
  2. 2. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CERTIFICATE Certified that this project report titled “ADVANCED JOINT BAYESIAN METHOD FOR FACE VERIFICATION ” is the bonafide work of Mr. _____________Who carried out the research under my supervision Certified further, that to the best of my knowledge the work reported herein does not form part of any other project report or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. Signature of the Guide Signature of the H.O.D Name Name
  3. 3. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com DECLARATION I hereby declare that the project work entitled “ADVANCED JOINT BAYESIAN METHOD FOR FACE VERIFICATION ” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the best of my knowledge, the work reported here is not a part of any other thesis or work on the basis of which a degree or award was conferred on an earlier occasion to me or any other candidate. (Student Name) (Reg.No) Place: Date:
  4. 4. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ACKNOWLEDGEMENT I am extremely glad to present my project “ADVANCED JOINT BAYESIAN METHOD FOR FACE VERIFICATION ” which is a part of my curriculum of third semester Master of Science in Computer science. I take this opportunity to express my sincere gratitude to those who helped me in bringing out this project work. I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.), PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project. I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from my deep heart for her valuable comments I received through my project. I wish to express my deep sense of gratitude to my guide Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for successful completion of this project. I also express my sincere thanks to the all the staff members of Computer science for their kind advice. And last, but not the least, I express my deep gratitude to my parents and friends for their encouragement and support throughout the project.
  5. 5. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com ABSTRACT: Generative Bayesian models have recently become the most promising framework in classifier design for face verification. However, we report in this paper that the joint Bayesian method, a successful classifier in this framework, suffers performance degradation due to its underuse of the expectation– maximization algorithm in its training phase. To rectify the underuse, we propose a new method termed advanced joint Bayesian (AJB). AJB has a good convergence property and achieves a higher verification rate than both the Joint Bayesian method and other state-of-the-art classifiers on the labeled faces in the wild face database.
  6. 6. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com INTRODUCTION: Face verification systems aim to find out whether two face images belong to the same subject. A typical face verifi- cation system consists of several stages: image preprocessing; feature extraction, selection and transformation; and classifi- cation. As the final stage, classification plays a critical role in the whole system. Most prevalent face-verification classifiers can be generally grouped into two categories. The first category is based on discriminative models, represented by metric or similarity learning algorithms, and one-shot learning algorithms. These algorithms focus their targets on searching appropriate similarity measures, which produce large values on sample pairs from the same subject and small values on sample pairs from different subjects. The second category is based on generative models, having PLDA, and the Joint Bayesian method, as representatives. These methods treat the samples as random variables respecting certain data-generating models, and treat the subjects of the samples as latent variables. Although it is still too early to say which category is supe rior, the latest reports on face verification seem to favor generative models, in particular the Joint Bayesian method.Three recent pieces of work, use this method to design the classifiers of their systems. DeepFace an innovative algorithm, which uses a deep learning structure and reduces the error of the state of the art by more than 25%, also states that the Joint Bayesian method is the “currently most successful system”. However, the Joint Bayesian method uses an EM-like algorithm to estimate the model parameters, which deviates from the standard EM algorithm. This algorithmic deviation, as we shall report in this paper, will cause the parameter estimator of the Joint Bayesian method to have some undesired properties. In specific, the method will converge to some degraded parameters in terms of verification performance. In this paper, we shall first demonstrate the above phenomena of Joint Bayesian, using simple experiments on both synthetic and real-world data. Then we shall propose to replace the parameter estimation algorithm of Joint Bayesian with a standard EM algorithm and develop a novel classifier design method. The new method, which we call Advanced Joint Bayesian (AJB), has some better properties than Joint Bayesian. The parameter estimation algorithm of AJB will be guaranteed to converge to appropriate model parameters and be insensitive to the initial settings. The computational burden due to the modification from Joint Bayesian to AJB is minor, and the model training times for both methods are nearly the same. Moreover, as shown by our experiment
  7. 7. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com results on the Labeled Faces in Wild (LFW) database , the performance of AJB is superior to the Joint Bayesian method. The rest of our paper is organized as follows. In Section II we theoretically derive the Joint Bayesian method as an implementation of the generative verification framework. In Section III we demonstrate the undesired properties of Joint Bayesian and propose our AJB method. In Section IV we show the effectiveness of AJB by applying it to the LFW face database and comparing it with both Joint Bayesian and other state-of-the-art classifiers. We conclude our paper in Section V.
  8. 8. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com CONCLUSION: In this paper we have reported that the Joint Bayesian method, a successful generative Bayesian classifier, suffers performance degradation due to its underuse of the EM algorithm in its training phase. To rectify this underuse, we have proposed a novel method named Advanced Joint Bayesian (AJB). Our AJB method can be guaranteed to converge to appropriate model parameters. In this paper, various experiments on the LFW database have demonstrated the superiority of AJB compared with the Joint Bayesian method for face verification.
  9. 9. OUR OFFICES @CHENNAI/ TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE / BANGALORE / HYDRABAD CELL: +91 9894917187 | 875487 1111 / 2111 / 3111 / 4111 / 5111 / 6111 ECWAY TECHNOLOGIES IEEE SOFTWARE | EMBEDDED | MECHANICAL | ROBOTICS PROJECTS DEVELOPMENT Visit: www.ecwaytechnologies.com | www.ecwayprojects.com Mail to: ecwaytechnologies@gmail.com REFERENCES: [1] M. Guillaumin, J. Verbeek, and C. Schmid, “Is that you? Metric learning approaches for face identification,” in Proc. IEEE 12th Int. Conf. Comput. Vis. (ICCV), Sep./Oct. 2009, pp. 498–505. [2] H. V. Nguyen and L. Bai, “Cosine similarity metric learning for face verification,” in Proc. 10th Asian Conf. Comput. Vis. (ACCV), 2010, pp. 709–720. [3] Y. Ying and P. Li, “Distance metric learning with eigenvalue optimization,” J. Mach. Learn. Res., vol. 13, no. 1, pp. 1–26, 2012. [4] Q. Cao, Y. Ying, and P. Li, “Similarity metric learning for face recognition,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2013, pp. 2408–2415. [5] L. Wolf, T. Hassner, and Y. Taigman, “Similarity scores based on background samples,” in Proc. 9th Asian Conf. Comput. Vis. (ACCV), 2009, pp. 88–97. [6] Y. Taigman, L. Wolf, and T. Hassner, “Multiple one-shots for utilizing class label information,” in Proc. Brit. Mach. Vis. Conf. (BMVC), 2009, pp. 1–12. [7] H. J. Seo and P. Milanfar, “Face verification using the LARK representation,” IEEE Trans. Inf. Forensics Security, vol. 6, no. 4, pp. 1275–1286, Dec. 2011. [8] S. Ioffe, “Probabilistic linear discriminant analysis,” in Proc. 9th Eur. Conf. Comput. Vis. (ECCV), 2006, pp. 531–542. [9] P. Li, Y. Fu, M. Mohammed, J. H. Elder, and S. J. D. Prince, “Probabilistic models for inference about identity,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 1, pp. 144–157, Jan. 2012. [10] D. Chen, X. Cao, L. Wang, F. Wen, and J. Sun, “Bayesian face revisited: A joint formulation,” in Proc. 12th Eur. Conf. Comput. Vis. (ECCV), 2012, pp. 566–579. [11] X. Cao, D. Wipf, F. Wen, G. Duan, and J. Sun, “A practical transfer learning algorithm for face verification,” in Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Dec. 2013, pp. 3208–3215. [12] D. Chen, X. Cao, F. Wen, and J. Sun, “Blessing of dimensionality: Highdimensional feature and its efficient compression for face verification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), Jun. 2013, pp. 3025–3032.

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