Face Detection: Unterschied zwischen den Versionen

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=== Summary ===
 
=== Summary ===
 
All in all we had a few successes in this first part, but we weren't satisfied, therefore we began to study papers and ideas in face detection and face recognition, which had come up in the scientific community so far.
 
All in all we had a few successes in this first part, but we weren't satisfied, therefore we began to study papers and ideas in face detection and face recognition, which had come up in the scientific community so far.
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== Face Detection Methods ==

Version vom 2. März 2010, 09:28 Uhr

First Steps

In the first small part of our project we didn’t start as usually with reading papers or anything like that: We tried to come up with our own ideas of how face detection can work. This was done without looking into literature to prevent to get influenced by ideas of other people or standard approaches.

Color Composition

One thing we looked at was the composition of the color of a given picture. With calculating average colors from different areas of the picture we find that it is possible to decide if the given picture is showing a face (portrait) or not. For this method it is necessary that a colored picture is available which can be investigated. When using this method it is very important that the picture/picture area, which is investigated, shows not only the face, but also the surrounding of it, i.e. some background, to have a difference between the face (red area) and the non-face area (green area).

Bild 01.jpg

With choosing color values from random pixels in the face area (red area in the image), calculating mean values and standard deviations and comparing these results one can decide if the investigated area has an face in the middle or not.

Lots of attempts to find the right choice for the query and the parameters lead to the result that it is necessary to have a lot of red color in the face area, compared to the mean value of all colors (red, blue and green). Red must dominate over more than 55 percent against the mean value for good results.

At this point it is very important to mention that this is only true if the investigated faces are from white-skinned people from Middle-Europe. For other skin-colors and other places in the world other values for the parameters must be chosen. A neural network should help a lot with finding the right values, but it wasn't implemented in our program.

An improvement of this method was reached by looking also at the color values of random pixels from the whole picture. With the knowledge of which difference should occur comparing the values of the whole image and just the area in the middle if the picture is showing a face another kind of decision could be found.

When using this method it is very important that the picture/picture area, which is investigated, shows not only the face, but also the surrounding of it, i.e. some background, to have a difference between the face (red area) and the non-face area (green area).

The decision if the picture is showing a face using this improved method is based again on the fact that the color red must dominate over the other colors and also over the color red in the outer region or rather when looking at the whole picture. The exact request can be found in our program, which is available here for download:

Download as ZIP-File

Other Methods

Another possibility which come to our minds was the detection of contrast in a picture to detect the circumference of a face.

Summary

All in all we had a few successes in this first part, but we weren't satisfied, therefore we began to study papers and ideas in face detection and face recognition, which had come up in the scientific community so far.

Face Detection Methods