Eigenface

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

Face recognition using OPRA-faces

Abstract: This paper presents a method named "orthogonal projection reduction by affinity", or OPRA-faces, for face recognition. As its name indicates, the method consists of an (explicit) orthogonal mapping from the data space to the reduced space. In addition, the method attempts to preserve the local geometry, i.e., the affinity of the points in a geometric representation of the data. The method starts by computing an affinity mapping W, of the data, which optimally expresses each point as a convex combination of a few nearest neighbors. This mapping can be viewed as an optimal representation of the intrinsic neighborhood geometries and is computed in a manner that is identical with the method of locally linear embedding (LLE). Next, and in contrast with LLE, the proposed scheme computes an explicit linear mapping between the high dimensional samples and their corresponding images in the reduced space, which is designed to preserve this affinity representation W. OPRA-faces shares some properties with Laplacianfaces, a recently proposed technique for face recognition, which computes the linear approximation of the Laplace-Beltrami operator on the image manifold. Laplacianfaces aims at preserving locality but does not explicitly consider the intrinsic geometries of the neighborhoods as does OPRA. As a result of the preservation of the affinity mapping W, OPRA will tend to produce a linear subspace which captures the essential geometric characteristics of the dataset. This feature, which appears to be crucial in representing images, makes the method very effective as a tool for face recognition. OPRA is tested on standard face databases and its effectiveness is compared with that of Laplacianfaces, eigenfaces and Fisherfaces. The experimental results indicate that the proposed technique produces results that are sharply superior to the other methods, at a comparable or lower cost.

Face recognition using Eigenfaces-Fisher Linear Discriminant and Dynamic Fuzzy Neural Network

Abstract: In order to solve the problem of face recognition in natural illumination, a new face recognition algorithm using Eigenface-Fisher Linear Discriminant (EFLD) and Dynamic Fuzzy Neural Network (DFNN) is proposed in this paper, which can solve the dimension of feature, and deal with the problem of classification easily. In this paper, we use EFLD model to extract the face feature, which will be considered as the input of the DFNN. And the DFNN is implemented as a classifier to solve the problem of classification. The proposed algorithm has been tested on ORL face database. The experiment results show that the algorithm reduces the dimension of face feature and finds a best subspace for the classification of human face. And by optimizing the architecture of dynamic fuzzy neural network reduces the classification error and raises the correct recognition rate. So the algorithm works well on face database with different expression, pose and illumination.

Development of a Face Recognition System and Its Intelligent Lighting Compensation Method for Dark-Field Application

Abstract: A face recognition system that uses 3-D lighting estimation and optimal lighting compensation for dark-field application is proposed. To develop the proposed system, which can realize people identification in a near scene dark-field environment, a light-emitting diode (LED) overhead light, eight LED wall lights, a visible light binocular camera, and a control circuit are used. First, 68 facial landmarks are detected, and their coordinates in both image and camera coordinate systems are computed. Second, a 3-D morphable model (3DMM) is developed after considering facial shadows, and a transformation matrix between the 3DMM and camera coordinate systems is estimated. Third, to assess lighting uniformity, 30 evaluation points are selected from the face. Sequencing computations of LED radiation intensity, ray reflection luminance, camera response, and face lighting uniformity are then carried out. Ray occlusion is processed using a simplified 3-D face model. Fourth, an optimal lighting compensation is realized: the overhead light is used for flood lighting, and the wall lights are employed as meticulous lighting. A genetic algorithm then is used to identify the optimal lighting of the wall lights. Finally, an Eigenface method is used for face recognition. The results show that our system and method can improve face recognition accuracy by >10% compared to traditional recognition methods.

Implementing Augmented Reality By Using Face Detection, Recognition And Motion Tracking

Abstract: Natural User Interface(NUI) technologies introduce new trends in using devices such as computer and any other electronic devices. In this paper, an augmented reality on a mobile device is implemented by using face detection, recognition and motion tracking. The face detection is obtained by using Viola-Jones algorithm from the images of the front camera. The Eigenface algorithm is employed for face recognition and face motion tracking. The augmented reality is implemented by overlapping the rear camera image and GPS, accelerator sensors` data with the 3D graphic object which is correspond with the recognized face. The algorithms and methods are limited by the mobile device specification such as processing ability and main memory capacity.