Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

Research on Face Recognition Based on PCA

Abstract: Principal components analysis (PCA) is a basic method widely used in face feature extraction and recognition. In order to overcome the shortcoming of absent consideration of the between-class information and the defect of the inconvenient update of the eigen-space in the traditional PCA method, this paper proposed a cluster-based feature projection method. The method enlarges the difference of samples in the different classes, while the difference of the same classes is reduced. Experimental results on ORL face database show that the method has higher correct recognition rate and higher recognition speeds than traditional PCA algorithm.

Face Recognition by PCA Technique

Abstract: Face recognition is one of the most active research areas in computer vision and pattern recognition with practical applications. This work proposes an apperence based Eigenface technique. PCA is used in extracting the relevant information in human faces. In this method the Eigen vectors of the set of training images are calculated which define the face space. Face images are projected on to the face space which encodes the variation among known face images. These encoded variations are used for recognition. Experiments are carried on IndianFace Database; the obtained recognition rate is 92.30%. The same training set is tested with nonface database.

Computing Eigenface from Edge Images for Face Recognition Based on Hausdorff Distance

Abstract: The different face regions have different degrees of importance for face recognition. In previous Hausdorff distance measures for face recognition, such as Spatially Eigen-Weighted Hausdorff distance (SEWHD) in which the weighting function is computed from grayscale images. However, Hausdorff distance is a measure for two binary point sets, not for grayscale point sets. Based on our previous work on Weighted Hausdorff distance (EFWHD) for face localization, a new weighting function of Hausdorff distance measure termed Edge Eigenface Weighted Hausdorff distance (EEWHD) is proposed for face recognition in this paper. The weighting function, which reflects the discriminative properties of face edge images effectively, is based on the eigenface of face edge images, not the eigenface of grayscale images in SEWHD, nor the edge points appearing frequency in EFWHD. The weighted Hausdorff distance Experimental results show the new method achieves higher recognition rate comparing with previous Hausdorff distance measures.

Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

Abstract: This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.