Eigenface

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

Design of radial basis function network as classifier in face recognition using eigenfaces

Abstract: In this paper we investigate alternative designs of a radial basis function network acting as classifier in a face recognition system. The inputs to the RBF network are the projections of a face image over the principal components. A database of 250 facial images of 25 persons is used for training and evaluation. Two RBF designs are studied: the forward selection and the Gaussian mixture model. Both designs are also compared to the conventional Euclidean and Mahalanobis classifiers. A set of experiments evaluates the recognition rate of each method as a function of the number of principal components used to characterize the image samples. The results of the experiments indicate that the Gaussian mixture model RBF achieves the best performance while allowing less neurons in the hidden layer. The Gaussian mixture model approach shows also to be less sensitive to the choice of the training set.

Automatic Location of Facial Feature Points and Synthesis of Facial Sketches Using Direct Combined Model

Abstract: Automatically locating multiple feature points (ie, the shape) in a facial image and then synthesizing the corresponding facial sketch are highly challenging since facial images typically exhibit a wide range of poses, expressions, and scales, and have differing degrees of illumination and/or occlusion When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex To resolve these problems, this paper develops an automatic facial sketch synthesis system based on a novel direct combined model (DCM) algorithm The proposed system executes three cascaded procedures, namely, (1) synthesis of the facial shape from the input texture information (ie, the facial image); (2) synthesis of the exaggerated facial shape from the synthesized facial shape; and (3) synthesis of a sketch from the original input image and the synthesized exaggerated shape Previous proposals for reconstructing facial shapes and synthesizing the corresponding facial sketches are heavily reliant on the quality of the texture reconstruction results, which, in turn, are highly sensitive to occlusion and lighting effects in the input image However, the DCM approach proposed in this paper accurately reconstructs the facial shape and then produces lifelike synthesized facial sketches without the need to recover occluded feature points or to restore the texture information lost as a result of unfavorable lighting conditions Moreover, the DCM approach is capable of synthesizing facial sketches from input images with a wide variety of facial poses, gaze directions, and facial expressions even when such images are not included within the original training data set

Comparison between face recognition algorithm-eigenfaces, fisherfaces and elastic bunch graph matching

Abstract: The technology of face recognition has become mature within these few years System, using the face recognition, has become true in real life In this paper, we will have a comparative study of three most recently methods for face recognition One of the approach is eigenface, fisherfaces and other one is the elastic bunch graph matching After the implementation of the above three methods, we learn the advantages and Disadvantages of each approach and the difficulties for the implementation

PCA-LDA based face recognition system & results comparison by various classification techniques

Abstract: Face recognition has a major impact in security measures which makes it one of the most appealing areas to explore. To perform face recognition, researchers adopt mathematical calculations to develop automatic recognition systems. As a face recognition system has to perform over wide range of database, dimension reduction techniques become a prime requirement to reduce time and increase accuracy. In this paper, face recognition is performed using Principal Component Analysis followed by Linear Discriminant Analysis based dimension reduction techniques. Sequencing of this paper is preprocessing, dimension reduction of training database set by PCA, extraction of features for class separability by LDA and finally testing by nearest mean classification techniques. The proposed method is tested over ORL face database. It is found that recognition rate on this database is 96.35% and hence showing efficiency of the proposed method than previously adopted methods of face recognition systems.