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27 April 2009 Multispectral face recognition using non linear dimensionality reduction
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Face recognition in the infrared spectrum has attracted a lot of interest in recent years. Many of the techniques used in infrared are based on their visible counterpart, especially linear techniques like PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). In this work, we introduce non linear dimensionality reduction approaches for multispectral face recognition. For this purpose, the following techniques were developed: global non linear techniques (Kernel-PCA, Kernel-LDA) and local non linear techniques (Local Linear Embedding, Locality Preserving Projection). The performances of these techniques were compared to classical linear techniques for face recognition like PCA and LDA. Two multispectral face recognition databases were used in our experiments: Equinox Face Recognition Database and Laval University Database. Equinox database contains images in the Visible, Short, Mid and Long waves infrared spectrums. Laval database contains images in the Visible, Near, Mid and Long waves infrared spectrums with variations in time and metabolic activity of the subjects. The obtained results are interesting and show the increase in recognition performance using local non linear dimensionality reduction techniques for infrared face recognition, particularly in near and short wave infrared spectrums.
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Moulay A. Akhloufi, Abdelhakim Bendada, and Jean-Christophe Batsale "Multispectral face recognition using non linear dimensionality reduction", Proc. SPIE 7341, Visual Information Processing XVIII, 73410J (27 April 2009);

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