This is an astonishing work. The paper proposed a system framework that can perform face identification and classification on large scale databases from the Internet that may contain ambiguous name labeling, a broad range of individuals and extremely varying photo conditions. Four main parts constitute the system: a simple yet effective data acquisition method, a robust face rectification, a scalable approximation of PCA and a reliable clustering algorithm.
I think the most important contribution of this work shoud be applying the Nystrom approximation to the PCA probem. Although this may not be purely novel ( as seen from the references), it does show that such an approximation is practical and can work well on real data. Many follow-up researches could then base on this result. The rectification, even if not highlighted in the paper, I think, is also vital for the success. The proposed algorithm can run in short time ( important for such a large database) and provide results with enough quality. The clustering algorithm is somehow straight-forward, but is OK for the purpose.
One weakness of the work is that it doesn't provide complete retrieval experimental results on the clustered data. The proposed measurement based on information theory appears reasonable, but it does not give an intuitive feeling of the data quality(I didn't understand it either :S). I am currently working on a problem that also deals with Internet photo collection and I believe the former parts of the paper did give me much inspiration.
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