HUMAN FACE IDENTIFICATION

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  • 1. Design Seminar on HUMAN FACE IDENTIFICATION Submitted for partial fulfillment of the degree of Bachelor of Engineering BY 1.Vishal Dhote 2.Bhupesh Lahare 3.Akash Bonde 4.Shrinath Wadyalkar 5.Nidhi Meshram 7th Semester Department of Information TechnologyEr.C.D.Bawankar Er. Ashvini Kheole Prof. S. V. Sonekar Project Guide Project Incharge HOD(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 Identification 2)Security 3)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 theory approach that decomposes face images into a small set of characteristic feature images called eigenfaces.• Recognition is performed by projecting a new image into the subspace[3].
  • 8. Process Flow Diagram: Start Login Authentication Valid User Invalid User Main Screen Add Image Clip Image Update Details Construct Image Search Process Enter Make Clips Open Record Specify Feature Search Image & Details & Update Get Details Add to Add Clips to Add to Search Result Database Database database Image End
  • 9. Context Flow Diagram: EYE WITNESS FACE OPERATOR IDENTIFICATION CRIMINAL SYSTEM FACE
  • 10. Login Process: PROCESS LOGIN SCREEN ERROR IN INPUT LEVEL-0
  • 11. Main Screen Process: MAIN OPERATOR SCREEN ADD IMAGE SEARCH IMAGE CLIP IMAGE CONSTRUCT IMAGE LEVEL-1
  • 12. Add Image Process: DATABASE ADD OPERATOR PROCESS DATA IS ADDED ERROR LEVEL-2
  • 13. Construct Image: DATABASE HAIR FOREHEADINSTRUCTION EYES FACE NOSE LIPS LEVEL-3
  • 14. Clipping Process: DATABASE DATABASE EYES NOSE FACE FACE HAIR FOREHEAD LEVEL-4
  • 15. Clipping Process:
  • 16. Update Process: DATABASE UPDATE DATA OPERATOR PROCESS UPDATED LEVEL-5
  • 17. Screenshot Face Identification Main Screen: LOGIN
  • 18. Screenshot Face Identification Main Screen: File
  • 19. Screenshot Face Identification Main Screen: File
  • 20. Screenshot Face Identification Main Screen: File
  • 21. Screenshot Face Identification Main Screen: File
  • 22. Screenshot Face Identification Main Screen: File
  • 23. Screenshot Face Identification Main Screen: EDIT
  • 24. Screenshot Face Identification Main Screen: EDIT
  • 25. ScreenshotFace Identification Main Screen: IDENTIFICATION
  • 26. ScreenshotFace Identification Main Screen: IDENTIFICATION
  • 27. ScreenshotFace Identification Main Screen: IDENTIFICATION
  • 28. Screenshot Face 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 Subject’s Face. 3.Low Resolution Images. 4.It is not platform independent
  • 32. Result: Thus we have reduced the problem of matching faces with previous applications. This application will find the approximate match of human face at various angles.
  • 33. Conclusion: A face recognition system must be able to recognize a face in many different imaging situations. It will find faces efficiently without exhaustively searching the image. Face recognition systems are going to have widespread 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