Abstract
This study proposes a framework to integrate the Gaborface features and the matrix based feature extraction method for face recognition. In this framework, we first select a subset of Gaborfaces to construct the optimal ensemble Gaborface. Then, a two-phase matrix based feature extraction method, i.e.: two-dimensional linear discriminant analysis (2DLDA) plus multi-subspaces principle component analysis (MSPCA), is developed to directly and effectively extract features from the optimal ensemble Gaborface matrixes. Experiment results on ORL and AR face datasets demonstrate the effectiveness of our method.
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Zhu, Q., Xu, Y., Lu, Y., Wen, J., Fan, Z., Li, Z. (2014). Novel Matrix Based Feature Extraction Method for Face Recognition Using Gaborface Features. In: Pan, JS., Krömer, P., Snášel, V. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 238. Springer, Cham. https://doi.org/10.1007/978-3-319-01796-9_38
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DOI: https://doi.org/10.1007/978-3-319-01796-9_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01795-2
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