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Facial feature localization

Facial feature localization. Presented by: Harvest Jang Spring 2002. Outline. Introduction Algorithm Evaluation Future work. Introduction. Face feature extraction Low-bit-rate video coding Human computer interaction Human face recognition Automatically facial features

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Facial feature localization

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  1. Facial feature localization Presented by: Harvest Jang Spring 2002

  2. Outline • Introduction • Algorithm • Evaluation • Future work

  3. Introduction • Face feature extraction • Low-bit-rate video coding • Human computer interaction • Human face recognition • Automatically facial features • Accuracy VS Performance

  4. Algorithm • Step 1: Check image is human face or not • Step 2: Find the face boundary • Step 3: Find the eye region • Step 4: Find the horizontal nose position • Step 5: Find the position of iris • Step 6: Find the vertical mouth position

  5. Human face checking • Use eigenface method • 40 images as training set • 15 eigenvector for representation • Subtract the image with the mean image • Projection the image to the eigenvector • Calculate the distance between the eigenvector and the projection image • Selecting the threshold to reject image

  6. Example Distance=5223 Distance=4992 Distance=7677 Distance=4544 Distance=3729 *can’t find face boundary Distance=4303 *can’t find eye region

  7. Face Boundary • Assume the picture is simple background • Use SOBEL filter for edge detection • Use horizontal projection of the binary image to find left and right face boundaries Sobel filter

  8. Eye Region • Use vertical projection to find possible eye region • Verify by property of symmetric of two eyes Vertical projection of the binary image

  9. Horizontal Nose Position • Use dynamic method to binaries the image • Find the selective threshold • Check the fill factor • Robust to skin color • Use horizontal projection of this binary image

  10. Dynamic binarization • Use intensity histogram to two peak • Skin intensity • Feature intensity • Calculate the threshold for binaries with fill factor feature intensity skin intensity Image histogram of the image

  11. Figure 1 Figure 2 Figure 3 Example Original image

  12. Determine the nose position • Use horizontal projection of the new binary image region • Characteristics • Three peak two valleys 3 peaks Black line: Final nose position 2 valleys Horizontal projection of the binary image region

  13. Position of iris • Divide the eye region into two parts • Compute normalized cross-correlation of image and the eye template at each part • Find the maximum value (max = 1) Left and right part of the eye region Correlation result Left and right eye template

  14. Position of mouth • Use the aspect ratio to find • Distance (d) between two eyes • Distance between the mouth and eye ( about 1.0d – 1.3d)

  15. Position of mouth • Use vertical projection • Find the minimum value binary image of mouth region Vertical projection of the binary image mouth region

  16. Evaluation • ORL face database • 40 subjects • 10 different photos for each subjects • Machine • Sun Ultra 5/400 • 97s for 400 photos

  17. Evaluation – ORL face database

  18. Future work • Improve the accuracy of finding iris • Detect human face from a large image • Detect face from video/web cam (face-tracking)

  19. Thank you!

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