feature extraction using PCA

My job is to perform gesture recognition. I want to do that by training a support vector machine using the features extracted by performing PCA(Principal component Analysis). But I'm getting a little confused about the procedure.

After going through various articles, I've figured out these steps.

1. Take 'd' number of images(n*n) of the same gesture.
2. Convert each n*n image into a sigle row.
3. Form a matrix of order d*(n*n).
4. Compute the eigen values & eigen vectors.
5. Use top 'k' e开发者_如何学编程igen vectors to form a subspace.
6. Project the image from original n*n dimension to 'k' dimension.

Question:

1) I have a set of 100 gestures and performing above 6 steps will give me 100 subspaces.My testing should be done on a realtime video to find which class a gesture falls in. Onto which supspace do I project each video frame to reduce the dimension for feeding it to the classifier?

Thank you in advance.