Abstract
This paper presents a novel supervised dimensionality reduction approach for facial feature extraction called (2D)2LDALPP. The proposed (2D)2LDALPP method effectively combines alternative 2DLDA with alternative 2DLPP. The feature extraction is split into two steps: firstly, the column directional information is extracted by applying alternative 2DLDA; secondly, the feature matrix is inversed and alternative 2DLPP is used to extract the row directional information. The advantage of the method lies in the compression of the facial image in two different directions and the fact that the dimension of the feature matrix is low. At the same time, because 2DLDA is a supervised learning method, the proposed method not only preserves the manifold structure of the samples but also contains the label information of the classes. Experimental results on the Feret, ORL, and Yale databases show that the proposed method is effective.
Highlights
Feature extraction is the key problem of face recognition, and the extraction of effective and stable features is a current research hot spot
The proposed method D LDALPP is used for face recognition and tested on three well‐known databases: Feret [25], ORL [26] and Yale [2]
On the ORL database, six images per individual are used as training samples and the remaining four images are used as testing samples
Summary
Feature extraction is the key problem of face recognition, and the extraction of effective and stable features is a current research hot spot. LDA used in face recognition will confront a small sample size problem (SSS) due to the larger size of the vector and the relatively small number of training samples To overcome this problem, the Fisherface method first projects the samples into the PCA space so that the within‐class scatter matrix is non‐ singular. Many studies have indicated that face images possibly reside in a low dimensional, non‐linear sub‐ manifold embedded in the original high dimensional data space. This discovery has inspired many to propose the use of many manifold learning methods in face recognition. He [3] proposed a Locality Preserving Projection (LPP) called the Laplacianfaces method, which www.intechopen.com
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