The document reviews face recognition techniques, including face detection, feature detection, and various methods such as knowledge-based, feature invariant, and template matching approaches. It discusses the historical development of these technologies, challenges faced in implementation, and the applications of biometric skin texture analysis. The significance of face detection in security and surveillance systems, as well as the process of face identification and verification, is also highlighted.

1. FACE RECOGNITOIN TECHNIQUEREVIEW-1By:Suvigya Tripathi (09BEC094) Ankit V. Gupta (09BEC106)Guided By:Prof. Bhupendra FataniyaDept. of Electronics and Communication Engineering,Nirma University at Ahmedabad.

2. What is face detection.

3. The History.

4. Challenges.

5. 2D-Image Scan.

6. Biometrics : Skin Texture Analysis.

7. Applications.OUTLINE

8. INTRODUCTIONSteps:

9. Face Detection: differentiate a human face from the background of the image or a real time video.

10. Feature Detection: record its features.

11. Face Recognition: Compare it to a data base.BLOCK DIAGRAM

12. Technique employed to distinguish a Human face from the rest of the background of the image.WHAT IS FACE DETECTION

13. During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson , worked on using the computer to recognize human faces.

14. He was proud of this work, but because the funding was provided by an unnamed intelligence agency that did not allow much publicity, little of the work was published.THE HISTORY

15. The first step for any automatic face recognition system system.

16. First step in many Human Computer Interaction systems.

17. Expression Recognition

18. Cognitive State/Emotional State Recognition

19. First step in many surveillance and security systems.

20. Video coding

21. Automatic Target Recognition(ATR)IMPORTANCE OF FACE DETECTION

22. In – Plane Rotation

23. Out – Plane Rotation

24. Lighting

25. Aging Effects

26. Facial Expressions

27. Face Covered by long Hairs or Hand.CHALLENGES

28. CHALLENGES

29. Different Approaches:Knowledge Based ApproachFeature Invariant MethodTemplate Matching Method2D – IMAGE SCAN

30. It uses human-coded rules to model facial features, such as two symmetric eyes, a nose in the middle and a mouth underneath the nose.KNOWLEDGE-BASED APPROACH

31. Pros: Easy to come up with simple rules Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified Work well for face localization in uncluttered background Cons:Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible casesKNOWLEDGE-BASED APPROACH-SUMMARY

32. Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.

33. Skin colors, edges and shapes fall into this category.FEATURE INVARIANT METHOD

34. FEATURE INVARIANT METHOD-NODAL POINT ANALYSISEvery face has numerous, distinguishable landmarks, the different peaks and valleys that make up the face

35. Distance between the eyes

36. Width of the nose

37. Depth of the eye sockets

38. The shape of the cheekbones

39. The length of the jaw linePros: Features are invariant to pose and change in orientation.Cons: Difficult to locate facial features due to several corruption (illumination, noise, occlusion) Difficult to detect features in complex backgroundFEATURE INVARIANT METHOD-SUMMARY

40. Template matching methods calculate the correlation between a test image and a pre-selected facial templates.TEMPLATE MATCHING METHOD

41. Pros: Simple Cons: Templates needs to be initialized near the face images Difficult to enumerate templates for different poses (similar to knowledge-based methods)TEMPLATE MATCHING METHOD-SUMMARY

42. Using skin color to find face segments is a vulnerable technique.

43. Non-animate objects with the same color as skin can be picked up since the technique uses color segmentation.Then the face can be picked up using any of the approaches.BIOMETRICSSKIN TEXTURE ANALYSIS

44. Lack of restriction to orientation or size of faces.

45. A good algorithm can handle complex backgrounds.

46. It is relatively insensitive to changes in expression, including blinking, frowning or smiling

47. Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.SKIN TEXTURE ANALYSIS:ADVANTAGES

48. Security measure at ATM’sDigital CamerasPublic Surveillance (CCTV’s) at Airports, Hospitals, etc.Televisions and computers can save energy by reducing the brightness.APPLICATIONS

49. A set of two task:

50. Face Identification: Given a face image that belongs to a person in a database, tell whose image it is.

51. Face Verification: Given a face image that might not belong to the database, verify whether it is from the person it is claimed to be in the database. FACE RECOGNITION:OVERVIEW

52. Wikipedia.org

53. How Stuff Works

54. www.crazyengineers.com

55. Kimmel, Ron. "Three-dimensional face recognition". Retrieved 2005-01-01.

56. ZiyouXiong, Univ. of Illinois at Urbana-ChampaignREFRENCE