Abstract
As a sub-field of pattern recognition, face recognition (or face classification) has become a hot research point. In pattern recognition and in image processing, feature extraction based no dimensionality reduction plays the important role in the relative areas. Feature extraction simplifies the amount of resources required to describe a large set of data accurately for classification and clustering. On the algorithms, when the input data are too large to be processed and it is suspected to be notoriously redundant (much data, but not much information), then the input data will be transformed into a reduced representation set of features also named features vector with linear transformation or the nonlinear transformation. Transforming the input data into the set of features is called feature extraction. If the features extracted are carefully chosen, it is expected that the features set will extract the relevant information from the input data in order to perform the desired task using this reduced representation instead of the full size input data.
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Li, JB., Chu, SC., Pan, JS. (2014). Kernel Principal Component Analysis (KPCA)-Based Face Recognition. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_4
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DOI: https://doi.org/10.1007/978-1-4614-0161-2_4
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