Abstract A method for infrared face recognition is proposed based on principal component analysis (PCA) and linear discriminant analysis (LDA). According to the characteristics of infrared face images, a set of normalized infrared face images is gotten by preprocessing. The dimensionality of the image vector is reduced and the global features are extracted. The global features are used to generate a classifier which can minimize the withinclass scatter and maximize the betweenclass scatter. Finally, an infrared face recognition experiment based on the combination of PCA and LDA is performed and the results show the high performance of the proposed method.
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2008, Vol. 21 
