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I have a large face database, and I want to search through it. For example, if I input a new face image, the system would return the best N similarities.

I know that PCA, LDA, and Procrustes analysis are used for this, but it seems that I need to project all this large database into PCA space, and I don't know any library that can handle that.

Are there some tools, software, or a library for this?

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If you can transform your problem into the following "given a set of vectors find the N ones with the smallest euclidian distance to the query", there are well-known methods (Ball-trees, Locality-sensitive hashing...) for solving it on large datasets. With this approach, PCA / LDA is thus just a pre-processing step for transforming the set of features produced by your feature extractor into a lower-dimensionality feature vector.

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I want to use model like ASM(active shape model) for face recognition. For example I mark by the hands points on 1st image and 2nd and then I want to compare them. What metric I should choose? My 1st assumption was to use simple euclidean distanse for 2D points, but before I need to "align" models for example calculate сetres of gravity and align, and then apply scale changes,also I can align by the axes of the ellipses, but I think it doesn't give good align results, I think I need some non linear cost function to "morph" from 1 model to another. –  mrgloom Nov 12 '12 at 10:32
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