Face Recognition: Results Summary
Image Pre-processing
We present a range of pre-processing techniques, which may affect the EER
of the eigenface system, when applied to face images prior to recognition.
The image processing methods fall into four main categories: colour normalisation
methods, statistical methods, convolution methods and combinations of these
methods. The methods are used to produce a single scalar value for
each pixel. Examples of these pre-processing methods can be seen
below.
Equal Error Rates
We present the results produced by using various image processing methods
as a bar chart of EERs. The base-line eigenface system (no image
processing) is displayed in the chart as a dark red bar. It can be
seen that the majority of image processing methods did produce some improvement
to the eigenface system. However, what is surprising is the large
increase in error rate produced by some of the colour normalisation methods
of image processing. An increase is also witnessed using the blurring
filters. It is therefore not surprising to see that the edge enhancing
methods had a positive impact on the EERs (the find edges and contour filters
were particularly effective), as did the statistical methods.
Tweaking
Having identified the most successful image processing method of those
evaluated (Local Brightness Contour), we continue to improve the system
by testing different cropping of images to find the optimum for this image
processing method, reaching an EER of 22.4%.
