New Feature Extraction Approaches for Face Recognition

All the traditional PCA-based and LDA-based methods are based on the analysis of vectors. So, it is difficult to evaluate the covariance matrices in such a high-dimensional vector space. Recently, two-dimensional PCA (2DPCA) and two-dimensional LDA (2DLDA) have been proposed in which image covariance matrices can be constructed directly using original image matrices. In contrast to the covariance matrices of traditional 1D approaches (PCA and LDA), the size of the image covariance matrices using 2D approaches (2DPCA and 2DLDA) are much smaller. As a result, it is easier to evaluate the covariance matrices accurately and computation cost is reduced. However, a drawback of 2D approaches is that it needs more coefficients than traditional approaches for image representation. Thus, 2D approach needs more memory to store its features and costs more time to calculate distance (similarity) in classification phase. In this paper, we develop a new image feature extraction methods called two-sta...

• Type: Conference
• Year: 2005
• Where: AUSAI
• Authors: Vo Dinh Minh Nhat, Sungyoung Lee
• Keywords: Artificial Intelligence, AUSAI 2005, Covariance Matrices, Image Covariance Matrices, Subspace Approaches