Face recognition based on PCA image reconstruction and LDA
OptikPublished 24 July 2013
Changjun Zhou, Lan Wang, Qiang Zhang, Xiaopeng Wei
Citations60
SJR quartileQ2
SJR score0.51
SNIP0.92
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TL;DR
A novel method based on PCA image reconstruction and LDA for face recognition is proposed, where the inner-classes covariance matrix for feature extraction is used as generating matrix and eigenvectors from each person are obtained, then the reconstructed images are obtained.
Abstract
Face recognition has become a research hotspot in the field of pattern recognition and artificial intelligence. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two traditional methods in pattern recognition. In this paper, we propose a novel method based on PCA image reconstruction and LDA for face recognition. First, the inner-classes covariance matrix for feature extraction is used as generating matrix and then eigenvectors from each person is obtained, then we obtain the reconstructed images. Moreover, the residual images are computed by subtracting reconstructed images from original face images. Furthermore, the residual images are applied by LDA to obtain the coefficient matrices. Finally, the features are utilized to train and test SVMs for face recognition. The simulation experiments illustrate the effectivity of this method on the ORL face database.
Keywords
Computer Science
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