Face recognition tech1
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Face recognition tech1

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

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

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  • 1. FACE RECOGNITOIN TECHNIQUE
    REVIEW-1
    By:Suvigya Tripathi (09BEC094)
    Ankit V. Gupta (09BEC106)
    Guided By:Prof. Bhupendra Fataniya
    Dept. 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. INTRODUCTION
    • Steps:
    • 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. long Hairs or Hand.
    CHALLENGES
  • 28. CHALLENGES
  • 29.
    • Different Approaches:
    Knowledge Based Approach
    Feature Invariant Method
    Template Matching Method
    2D – 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 cases
    KNOWLEDGE-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 ANALYSIS
    • Every 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 line
    • Pros:
    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 background
    FEATURE 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’s
    Digital Cameras
    Public 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-Champaign
    REFRENCE