Face Detection

We tested the method with a set of 30 images. The achieved classification rate was 76%. Most of the misses included regions that had very similar skin likelihood values and regions that were indeed skin regions but they were very high, such as the arms and legs with more than one hole in the upper part of the skin region. Other misses happened due the constrain we set of having one or more holes in a skin region in order to process that region.

We present some images and their corresponding processing in order to detect if there is any face in the image.

In Figure 15, we see that the neck of the lady is long, and this might cause that we detect the neck also. As described in the previous section, we set the ratio to be 1.6 and the height decreased accordingly. Notice also that the template face was fitted into the skin region very accurately, giving a cross correlation value grater than 0.8.
 

Original Image	Skin-likelihood Image	Skin-segmented Image	Image and Template Face	Final Detection
Figure 15. Image processing sequence for face detection for the image "blackgirl.jpg"

In Figure 16, we see that the child has blond hair, which in this case is very similar to the child's face color. This results in having a large skin region as shown in the third image. Consequently, the face model was fit in a larger area then the child's face. The region was detected with a cross correlation value of 0.71
 

Original Image	Skin-likelihood Image	Skin-segmented Image	Image and Template Face	Final Detection
Figure 16. Image processing sequence for face detection for the image "blackgirl.jpg"

In Figure 17, we see an image that was neat and easy to be detected. The woman skin region has 2 holes (the eyes are not included). The man has 5, and the baby has 2. The cross correlation value for the three of them was greater than 0.8.
 

Original Image	Skin-likelihood Image	Skin-segmented Image	Image and Template Face	Final Detection
Figure 17. Image processing sequence for face detection for the image "blackgirl.jpg"

Figure 18 was a bit more complicated since the skin region corresponding to the man only presented one hole (not even noticed here), but the cross correlation value was greater than 0.85 and this resulted in a good classification.
 

Original Image	Skin-likelihood Image	Skin-segmented Image	Image and Template Face	Final Detection
Figure 18. Image processing sequence for face detection for the image "chinesecouple.jpg"

In Figure 19, we can appreciate that our implementation can classify faces of different races. The skin segmentation was accurate. The cross-correlation value was around 0.7. Notice that the template face is a little bit off the real face. This is for the center of the mass was to the left of the nose of the lady. The reason for this is that the left part of the image has a larger skin area than the right part (notice the opening in the hair to the left).
 

Original Image	Skin-likelihood Image	Skin-segmented Image	Image and Template Face	Final Detection
Figure 19. Image processing sequence for face detection for the image "naomi.jpg"

Finally, Figure 20 illustrates 2 human faces of slightly different skin colors. Notice that the hands and the cat regions were not detected since the ratio was lower than 0.8 (wider than higher), which does not correspond to a human face region. In both faces, the template face was elongated a little bit due to the height to width ratio.
 

Original Image	Skin-likelihood Image	Skin-segmented Image	Image and Template Face	Final Detection
Figure 20. Image processing sequence for face detection for the image "women.jpg"

Next: Conclusion Previous: Template Matching Contents: Face Detection