Face detection using template matching

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Face detection using template matching

  1. 1. LOGO Presented 110060131048 Computer Science & Engg. Face Detection Using Template Matching By Brijesh Borad
  2. 2.  What is Face Detection?  Given an image, tell whether there is any human face or not.  If there is then find the location and size of each human face in the image.  Classification between face and non-face. Results for Training_1.jpg
  3. 3.  Difference between Face Detection and Face RecognitionFD:-Only two classifications face or non face FR:- have multiple classifications, adjusted by the number of individuals who want to be recognized. One person vs. all the others  Classification from face shape, of form eyes, nose, mouth, etc.  FR process requires the first FD
  4. 4.  Why Face Detection is Important? First step for any automatic face recognition system. First step in many Human-Computer Interaction systems and Man-machine Interaction. • Expression Recognition • Cognitive State/Emotional State Recogntion  First step in many surveillance systems Tracking: Face is a highly non rigid object. A step towards Automatic Target Recognition(ATR) or generic object detection/recognition.
  5. 5.  Methods for Face Detection  Knowledge-based methods:  Encode what constitutes a typical face. e.g., the relationship between facial features  Feature invariant approaches:  Aim to find structure features of a face that exist even when pose, viewpoint or lighting conditions vary  Template Matching:  Several standard patterns stored to describe the face as a whole or the facial features separately  Appearance-based methods:  The models are learned from a set of training images that capture the representative variability of faces. 1 2 3
  6. 6.  System Architecture For Face Detection Input RGB JPEG image Average Face Skin Segmentation RGB to YCbCr RGB to HSV Threshold to determine skin regions Multi- resolution Iterative Template Matching Classifier Skin Pixels Face/ Non-Face
  7. 7.  Flowchart for Face detection Input Image Find Skin Regions Count number of holes Detected Face region Template Matching Skin Segmention Non-FaceHoles >=1
  8. 8.  Process of finding Skin pixels in image.  Reject as much “non-skin” of the image as possible.  Detects Skin region in images and remove background parts using threshold.  Remove other body parts by applying binary image processing.  Skin Segmentation and Thresholding
  9. 9.  Template Matching A Technique for finding small parts of an image which match a template image. Used to detect the more accurate faces, and neglect the non-faces Determine the face locations such as based on the correlation values. Resize and rotate template image as requirement for finding human face in image.
  10. 10. Stored Template image Rotate template Searching In Different Size Modes Eye and Mouth Templates search for the eye and the mouth location  Process of Template Matching
  11. 11. Template face Rotated Template face 1. A skin region 2. skin region without holes 3. Template face is located in the center of the skin region
  12. 12. 5. As in (4), but inverted4. As in figure (3) but with a hole in the template face 6. Previous image is multipled by original one As in (6), but adding the Template face to it. 8. Final Result
  13. 13.  Face Detection : Current State  Front-view face detection can be done at more than 15 frames per second on 320x240 black-and-white images on a 700MHz PC with ~95% accuracy.  Detection of faces is faster than detection of edges!  Side view face detection remains to be difficult.
  14. 14.  Face Detection : Challenges  Templates needs to be initialized near the face images.  Difficult to enumerate templates for different poses  Out-of-Plane Rotation: frontal, 45 degree, profile, upside down  Facial Expressions  In-Plane Rotation  Image conditions: • Size • Lighting condition • Distortion • Noise • Compression

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