Face Detection using PCA.

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.

Eigen Vector

Today i just created one application to demonstrate the use of eigen vectors.

It is only for 2 diamensional things. Beacuse in 3D calculating eigen vector is a complex process ( It depends), involving linear equation solving , and cubic equations' root finding.

You canse see the eigen axis can be used for finding the OBB( oriented bounding boxes) for a set of points. This has significance inmportant in Computer graphics.

One more interesting thing is eigen vector of a rotaion matrix is actually its axis of rotation (similar to curl of a vector field , but curl calculation is easier).