I have been working on my image processing library to support face detection. I started with basic method PCA ( Principal component analysis). Basically you need to have a set of images(20-50) with different lighting conditions etc. The next step is to create covariance matrix out of it and find the eigen vector. Then simply project the the image you need to check in to Eigen vectors and find the distance between them.Do some thresholding to classify it.One important thing is you don't have to take all eigen vectors,may be its better to sort (descending ) based on Eigen value and take only first 'N' vectors.
See the video
The difficult part in PCA may be to find the eigne vectors , QA algorithm seems a good choice. The current problem with running time.It takes almost 1 second to process 200X201 image. Roughly O(n^3) complexity. I am plan to implement it in CUDA,so that it can be used for real time detection.
