Face detection

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Introduction
In recent years, face recognition has attracted much attention and its research has rapidly expanded by
not only engineers but also neuroscientists, since it has many potential applications in computer vision
communication and automatic access control system. Especially, face detection is an important part of
face recognition as the first step of automatic face recognition. However, face detection is not
straightforward because it has lots of variations of image appearance, such as pose variation (front,
non-front), occlusion, image orientation, illuminating condition and facial expression


Color Segmentation
Detection of skin color in color images is a very popular and useful technique for face detection.
Many techniques [12], [13] have reported for locating skin color regions in the input image. While the
input color image is typically in the RGB format, these techniques usually use color components in the
color space, such as the HSV or YIQ formats. That is because RGB components are subject to the
lighting conditions thus the face detection may fail if the lighting condition changes


Building Eigenimage Database
In order to save time to magnify or shrink an eigenimage to meet the size of the test image, a group of
eigenimages was stored in the database so that an appropriate eigenimage can be called with ease
without going through image enlarging or shrinking process. The eigenimages were stored in 20 files
from 30 pixel-width square image to 220 pixel-width square image with 10-pixel step. The stored
eigenimages were normalized by means of dividing the image matrix by its 2nd norm so that the effect
of eigenimage size does not affect the face detection algorithm.

Test Image Selection
After the color-based segmentation process, skin-colored area can be taken apart as shown in Fig. 6.
Given this binary image, a set of small test images needs to be selected and passed to the image
matching algorithm for the further process. The result of image selection solely based on the color
information is shown in Fig. 10. A square box was applied on each segment with the quantified
window size which was selected to meet the size of a face.