Localized Principal Component Analysis Learning for Face Feature Extraction and Recognition
نویسندگان
چکیده
We present a novel face feature extraction approach using localized Principal Component Analysis (PCA) learning in face recognition tasks. The localized PCA approach produces a set of fine-tuned feature specific masks from a constrained subset of the input distribution. This method is a guided-learning based on a set of pre-defined feature points over a short training sequence. The result is the set of eigenfeatures specifically tailored for face recognition. The procedure and result of our feature extraction approach and face recognition are illustrated and discussed.
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