Comparing Robustness of Two-Dimensional PCA and Eigenfaces for Face Recognition (original) (raw)
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Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition
Ieice Transactions, 2006
In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method. Based on Two-Dimensional Principle Component Analysis (2DPCA), face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCAbased technique over the conventional PCA technique.
In this paper, two-dimensional principal com- ponent analysis (2D PCA) is compared with other algo- rithms like 1D PCA, Fisher discriminant analysis (FDA), independent component analysis (ICA) and Kernel PCA (KPCA) which are used for image representation and face recognition. As opposed to PCA, 2D PCA is based on 2D image matrices rather than 1D vectors, so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices and its Eigen vectors are derived for image feature extraction. To test 2D PCA and evaluate its performance, a series of experiments are performed on three face image databases: ORL, Senthil, and Yale face databases. The recognition rate across all trials higher using 2D PCA than PCA, FDA, ICA and KPCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2D PCA than PCA.
2006 International Conference on Emerging Technologies, 2006
A facial recognition system is a system for automatically recognizing a person from a digital image as the human eye recognizes. Here two algorithms Principal Component Analysis (PCA) and Fisher Discriminant Analysis (FDA) of holistic approach of Information theory have been analyzed. Recognition process comprises the two steps: training and testing. In the training phase a set of the eigenvectors of the covariance matrix of the images used for training. These eigenvectors are also called as eigenfaces. In testing phase when a new input image is given for recognition, this image will be projected into the eigenspace by using the already calculated eigenvectors. Test image will be compared with all the images in the eigenspace and measures the euclidean distance. The image with the lowest euclidean distance is the matched image if the distance lies below some threshold value. Both algorithms works in the same manner, the difference lies in the calculation of face space. These two algorithms are evaluated experimentally on two databases each with the moderate subject size. Analysis and experimental results indicates that the PCA works well when the lightening variation is small. FDA works gives better accuracy in facial expression.
Discriminant Analysis for Recognition of Human Face Images (Invited Paper)
Journal of the Optical Society of America a Optics and Image Science, 1997
The discrimination power of various human facial features is studied and a new scheme for automatic face recognition (AFR) is proposed. The first part of the paper focuses on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain. This analysis allows objective evaluation of the significance of visual information in different parts (features) of the face for identifying the human subject. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class variations and minimizing within-class variations. The result is an efficient projection-based feature-extraction and classification scheme for AFR. Each projection creates a decision axis with a certain level of discrimination power or reliability. Soft decisions made based on each of the projections are combined, and probabilistic or evidential approaches to multisource data analysis are used to provide more reliable recognition results. For a medium-sized database of human faces, excellent classification accuracy is achieved with the use of very-low-dimensional feature vectors. Moreover, the method used is general and is applicable to many other image-recognition tasks. © 1997 Optical Society of America [S0740-3232(97)01008-9]
Bilinear Discriminant Analysis for Face Recognition
Lecture Notes in Computer Science, 2005
In this paper, a new statistical projection method called Bilinear Discriminant Analysis (BDA) is presented. The proposed method efficiently combines two complementary versions of Two-Dimensional-Oriented Linear Discriminant Analysis (2DoLDA), namely Column-Oriented Linear Discriminant Analysis (CoLDA) and Row-Oriented Linear Discriminant Analysis (RoLDA), through an iterative algorithm using a generalized bilinear projectionbased Fisher criterion. A series of experiments was performed on various international face image databases in order to evaluate and compare the effectiveness of BDA to RoLDA and CoLDA. The experimental results indicate that BDA is more efficient than RoLDA, CoLDA and 2DPCA for the task of face recognition, while leading to a significant dimensionality reduction.
Diagonal Fisher linear discriminant analysis for efficient face recognition
Neurocomputing, 2006
In this paper, a novel subspace method called diagonal Fisher linear discriminant analysis (DiaFLD) is proposed for face recognition. Unlike conventional principal component analysis and FLD, DiaFLD directly seeks the optimal projection vectors from diagonal face images without image-to-vector transformation. The advantage of the DiaFLD method over the standard 2-dimensional FLD (2DFLD) method is, the former seeks optimal projection vectors by interlacing both row and column information of images while the latter seeks the optimal projection vectors by using only row information of images. Our test results show that the DiaFLD method is superior to standard 2DFLD method and some existing well-known methods. r
Double Discriminant Analysis for Face Recognition
2009
Summary Feature selection for face representation is one of the central issues for any face recognition system. Finding a lower dimensional feature space with enhanced discriminating power is one of the important tasks. The traditional subspace methods represent each face image as a point in the disciminant subspace that is shared by all faces of different subject (classes). Such type of representation fails to accurately represent the most discriminate features related to one class of face, so in order to extract features that capture a particular class's notion of similarity and differ much from remaining classes is modeled. In this paper we propose a new method called "Double Discriminant Analysis" Which first performs PCA (Principal Component Analysis) to reduce the sample size and extract the features that separates individual class faces maximally. Then by projecting these samples over to the null space of within class matrix the intra class variance is reduced ...
Discriminant analysis for recognition of human face images
Journal of the Optical Society of America A, 1997
The discrimination power of various human facial features is studied and a new scheme for automatic face recognition (AFR) is proposed. The first part of the paper focuses on the linear discriminant analysis (LDA) of different aspects of human faces in the spatial as well as in the wavelet domain. This analysis allows objective evaluation of the significance of visual information in different parts (features) of the face for identifying the human subject. The LDA of faces also provides us with a small set of features that carry the most relevant information for classification purposes. The features are obtained through eigenvector analysis of scatter matrices with the objective of maximizing between-class variations and minimizing within-class variations. The result is an efficient projection-based feature-extraction and classification scheme for AFR. Each projection creates a decision axis with a certain level of discrimination power or reliability. Soft decisions made based on each of the projections are combined, and probabilistic or evidential approaches to multisource data analysis are used to provide more reliable recognition results. For a medium-sized database of human faces, excellent classification accuracy is achieved with the use of very-low-dimensional feature vectors. Moreover, the method used is general and is applicable to many other image-recognition tasks. © 1997 Optical Society of America [S0740-3232(97)01008-9]
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's Linear Discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The Eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the Eigenface technique for tests on the Harvard and Yale Face Databases.