Face recognition tech1


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this explains the face detection technique....we will cover face recognition in next upload...thank you..

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Face recognition tech1

  1. 1. FACE RECOGNITOIN TECHNIQUE<br />REVIEW-1<br />By:Suvigya Tripathi (09BEC094)<br /> Ankit V. Gupta (09BEC106)<br />Guided By:Prof. Bhupendra Fataniya<br />Dept. of Electronics and Communication Engineering,<br />Nirma University at Ahmedabad.<br />
  2. 2. <ul><li>What is face detection.
  3. 3. The History.
  4. 4. Challenges.
  5. 5. 2D-Image Scan.
  6. 6. Biometrics : Skin Texture Analysis.
  7. 7. Applications.</li></ul>OUTLINE<br />
  8. 8. INTRODUCTION<br /><ul><li>Steps:
  9. 9. Face Detection: differentiate a human face from the background of the image or a real time video.
  10. 10. Feature Detection: record its features.
  11. 11. Face Recognition: Compare it to a data base.</li></li></ul><li>BLOCK DIAGRAM<br />
  12. 12. <ul><li>Technique employed to distinguish a Human face from the rest of the background of the image.</li></ul>WHAT IS FACE DETECTION<br />
  13. 13. <ul><li>During 1964 and 1965, Bledsoe, along with Helen Chan and Charles Bisson , worked on using the computer to recognize human faces.
  14. 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.</li></ul>THE HISTORY<br />
  15. 15. <ul><li>The first step for any automatic face recognition system system.
  16. 16. First step in many Human Computer Interaction systems.
  17. 17. Expression Recognition
  18. 18. Cognitive State/Emotional State Recognition
  19. 19. First step in many surveillance and security systems.
  20. 20. Video coding
  21. 21. Automatic Target Recognition(ATR)</li></ul>IMPORTANCE OF FACE DETECTION<br />
  22. 22. <ul><li>In – Plane Rotation
  23. 23. Out – Plane Rotation
  24. 24. Lighting
  25. 25. Aging Effects
  26. 26. Facial Expressions
  27. 27. Face Covered by </li></ul> long Hairs or Hand.<br />CHALLENGES<br />
  28. 28. CHALLENGES<br />
  29. 29. <ul><li>Different Approaches:</li></ul>Knowledge Based Approach<br />Feature Invariant Method<br />Template Matching Method<br />2D – IMAGE SCAN<br />
  30. 30. <ul><li>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.</li></ul>KNOWLEDGE-BASED APPROACH<br />
  31. 31. <ul><li>Pros: </li></ul>Easy to come up with simple rules <br />Based on the coded rules, facial features in an input image are extracted first, and face candidates are identified <br />Work well for face localization in uncluttered background <br /><ul><li>Cons:</li></ul>Difficult to translate human knowledge into rules precisely: detailed rules fail to detect faces and general rules may find many false positives <br />Difficult to extend this approach to detect faces in different poses: implausible to enumerate all the possible cases<br />KNOWLEDGE-BASED APPROACH-SUMMARY<br />
  32. 32. <ul><li>Feature invariant methods try to find facial features which are invariant to pose, lighting condition or rotation.
  33. 33. Skin colors, edges and shapes fall into this category.</li></ul>FEATURE INVARIANT METHOD<br />
  34. 34. FEATURE INVARIANT METHOD-NODAL POINT ANALYSIS<br /><ul><li>Every face has numerous, distinguishable landmarks, the different peaks and valleys that make up the face
  35. 35. Distance between the eyes
  36. 36. Width of the nose
  37. 37. Depth of the eye sockets
  38. 38. The shape of the cheekbones
  39. 39. The length of the jaw line</li></li></ul><li><ul><li>Pros: </li></ul>Features are invariant to pose and change in orientation.<br /><ul><li>Cons: </li></ul>Difficult to locate facial features due to several corruption (illumination, noise, occlusion) <br />Difficult to detect features in complex background<br />FEATURE INVARIANT METHOD-SUMMARY<br />
  40. 40. <ul><li>Template matching methods calculate the correlation between a test image and a pre-selected facial templates.</li></ul>TEMPLATE MATCHING METHOD<br />
  41. 41. Pros: <br />Simple <br />Cons: <br />Templates needs to be initialized near the face images <br />Difficult to enumerate templates for different poses (similar to knowledge-based methods)<br />TEMPLATE MATCHING METHOD-SUMMARY<br />
  42. 42. <ul><li>Using skin color to find face segments is a vulnerable technique.
  43. 43. Non-animate objects with </li></ul>the same color as skin can <br />be picked up since the <br />technique uses color <br />segmentation.<br /><ul><li>Then the face can be picked up using any of the approaches.</li></ul>BIOMETRICSSKIN TEXTURE ANALYSIS<br />
  44. 44. <ul><li>Lack of restriction to orientation or size of faces.
  45. 45. A good algorithm can handle complex backgrounds.
  46. 46. It is relatively insensitive to changes in expression, including blinking, frowning or smiling
  47. 47. Has the ability to compensate for mustache or beard growth and the appearance of eyeglasses.</li></ul>SKIN TEXTURE ANALYSIS:ADVANTAGES<br />
  48. 48. Security measure at ATM’s<br />Digital Cameras<br />Public Surveillance (CCTV’s) at <br />Airports, Hospitals, etc.<br />Televisions and computers can <br />save energy by reducing the <br />brightness.<br />APPLICATIONS<br />
  49. 49. <ul><li>A set of two task:
  50. 50. Face Identification: Given a face image that belongs to a person in a database, tell whose image it is.
  51. 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. </li></ul>FACE RECOGNITION:OVERVIEW<br />
  52. 52. <ul><li>Wikipedia.org
  53. 53. How Stuff Works
  54. 54. www.crazyengineers.com
  55. 55. Kimmel, Ron. "Three-dimensional face recognition". Retrieved 2005-01-01.
  56. 56. ZiyouXiong, Univ. of Illinois at Urbana-Champaign</li></ul>REFRENCE<br />