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=5223Distance=4992Distance=7677 Distance=4544Distance=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 skin intensity feature intensity Image histogram of the image

11. Example Original image Figure 1 Figure 3 Figure 2

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

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 eye template Correlation result Left and right part of the eye region

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 Vertical projection of the binary image mouth region binary image of 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 Correct #Error rate(%) Eye region3931.75 Nose pos3766.00 Left iris33616.00 Right iris31222.00 Mouth pos32119.75

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!