1. Design SeminaronHUMAN FACE IDENTIFICATIONSubmitted for partial fulfillment of the degree ofBachelor of Engineering BY 1.Vishal Dhote2.Bhupesh Lahare 3.Akash Bonde 4.Shrinath Wadyalkar5.Nidhi Meshram7th SemesterDepartment of Information TechnologyEr.C.D.BawankarEr. Ashvini KheoleProf. S. V. SonekarProject Guide Project InchargeHOD(CSE/IT)Department of Information Technology,J D College of Engineering & Management, Nagpur.Rashtrasant Tukdoji Maharaj Nagpur University, Nagpur.Session: 2012-2013

2. Contents: Aim Objective Literature Survey -Problem Definition Research Methodology Software Requirements Hardware Requirements Limitations Result Conclusion Bibliography 3. Aim: Face recognize that works under varying poses. Importance of faces Central role in human interactions Communicate a wealth of social information: Age, gender, personal identity (physical structure) Mood and emotional state (facial expression) 4. Objective Develope a computational model. Why face recognition?To apply it to wide area of problems.1)Criminal Identification2)Security3)Image and Film Processing 5. Literature Survey:1. Avinash Kaushal1, J P S Raina, A., Face Detection using Eigenface method ,Gabor Wavelet Transform, IJCST Vol. 1, Iss ue 1, September 2010 I S S N : 0 9 7 6 - 8 4 9 1 Eigenface method, template matching, graph matching, method. The eigenface approach applies the Karhonen-Loeve transform for feature extraction. It greatly reduces the facial feature dimension and yet maintains reasonable discriminating power.2. Steve Lawrence , Lee Giles Face Recognition: A Convolutional Eigenface method IEEE Transactions on special issue on Pattern Recognition. vol.3, no110, 2009 Eigenface method, though some variants of the algorithm work on feature extraction as well, mainly provides sophisticated modeling scheme for estimating likelihood densities in the pattern recognition phase. 6. Problem Definition : To retrieve the similar images(based on a heuristic)from the given database of face images. It used to take much time to find any criminals Not very much accurate. Danger of losing the files in some case. 7. Research Methodology: Eigen face method is based on an information theoryapproach that decomposes face images into a small setof characteristic feature images called eigenfaces. Recognition is performed by projecting a new imageinto the subspace[3]. 8. Process Flow Diagram: Start LoginAuthentication Valid User Invalid UserMain Screen Add Image Clip Image Update Details Construct Image Search ProcessEnterMake ClipsOpen Record Specify Feature Search Image &Details & Update Get Details Add toAdd Clips toAdd toSearchResultDatabase Database database Image End 9. Context Flow Diagram: EYE WITNESS FACE OPERATOR IDENTIFICATION CRIMINALSYSTEM FACE 10. Login Process: PROCESS LOGINSCREEN ERROR ININPUT LEVEL-0 11. Main Screen Process: MAIN OPERATOR SCREENADD IMAGE SEARCHIMAGECLIP IMAGECONSTRUCTIMAGELEVEL-1 12. Add Image Process:DATABASEADD OPERATOR PROCESS DATA ISADDED ERRORLEVEL-2 13. Construct Image:DATABASEHAIRFOREHEADINSTRUCTION EYES FACENOSELIPS LEVEL-3 14. Clipping Process:DATABASEDATABASE EYES NOSEFACEFACE HAIR FOREHEAD LEVEL-4 15. Clipping Process: 16. Update Process:DATABASEUPDATEDATA OPERATORPROCESSUPDATED LEVEL-5 17. ScreenshotFace Identification Main Screen: LOGIN 18. ScreenshotFace Identification Main Screen: File 19. ScreenshotFace Identification Main Screen: File 20. ScreenshotFace Identification Main Screen: File 21. ScreenshotFace Identification Main Screen: File 22. ScreenshotFace Identification Main Screen: File 23. ScreenshotFace Identification Main Screen: EDIT 24. ScreenshotFace Identification Main Screen: EDIT 25. ScreenshotFace Identification Main Screen: IDENTIFICATION 26. ScreenshotFace Identification Main Screen: IDENTIFICATION 27. ScreenshotFace Identification Main Screen: IDENTIFICATION 28. ScreenshotFace Identification Main Screen: HELP 29. Software Requirements: Language : VB.Net Operating System : Windows Database: SQL Server 2005 30. Hardware Requirements: Processor : Processor with 400 Mhz. Hard disk : 1 GB hard disk. RAM: 256MB Mouse: MS mouse or compatible. Keyboard : standard 101 or 102 Keys. 31. Limitations: Face Recognition Is Not Perfect And Struggles To Perform Under Certain Conditions. 1. Poor Lighting 2.Other Objects Partially Covering The SubjectsFace. 3.Low Resolution Images. 4.It is not platform independent 32. Result: Thus we have reduced the problem of matching faceswith previous applications. This application will find the approximate match ofhuman face at various angles. 33. Conclusion: A face recognition system must be able to recognize aface in many different imaging situations. It will find faces efficiently without exhaustivelysearching the image. Face recognition systems are going to havewidespread application in smart environments.. 34. Bibliography:[1] Avinash Kaushal1, J P S Raina, A., Face Detection using NeuralNetwork & Gabor Wavelet Transform, IJCST Vol. 1, Iss ue 1,September 2010 I S S N : 0 9 7 6 - 8 4 9 1[2]Steve Lawrence , Lee Giles Face Recognition: A ConvolutionalNeural Network Approach IEEE Transactions on Neural Networks,Special Issue on Neural Networks and Pattern Recognition. vol.3,no110, 2009[3] Parvinder S. Sandhu, Iqbaldeep Kaur, Face Recognition UsingEigen face Coefficients and Principal Component Analysis,International Journal of Electrical and Electronics Engineering 3:82009 ISSN 0978-9481[4] Stan Z. Li and Juwei Lu., Face Recognition Using the NearestFeature Line Method , IEEE TRANSACTIONS ON NEURALNETWORKS, VOL. 10, NO. 2, MARCH 1999 pp-439-443[5] S. T. Gandhe, K. T. Talele, and A.G.Keskar Face RecognitionUsing Contour Matching IAENG International Journal of ComputerScience, 35:2, IJCS_35_2_06 35. Thank You