Eigenface

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

Pre-clustered principal component analysis for fast training of new face databases

Abstract: Generally the eigenface approach to obtain facial representation for face recognition requires the large computational load originated from solving eigenvalue problem to obtain principal axes. When the new face database is large, this computational load significantly increases. To resolve this difficulty, we propose a novel feature extraction scheme using pre-clustered face data; before a data set for specific problem (on-line data) is given, a number of general face images (off-line data) are clustered by a modified k-means clustering algorithm and a set of principal axes are obtained for each cluster by applying the principal component analysis to the data belonging to each cluster; when a data set for specific problem is given, an appropriate cluster is assigned to this data set by distance measure and features are extracted by using the principal axes of the selected cluster. With this scheme we can reduce the large portion of computational load required to find the principal axes for each on-line data set, with a slight degradation of classification performance. This performance degradation is compensated by using the class-augmented principal component analysis as an on-line feature extractor and nearest neighborhood classifier as a classifier. In experiments, we use Yale face database and ORL face database to compare results of recognition performance and computational efficiency with those of other previous results.

Face recognition using eigenface with naive Bayes

Abstract: One of the lack of eigenface for prediction the face recogniton is not good accuracy. This paper uses naive Bayes for classifying the result of eigenface feature extraction to predict the face. The normalization z-score is added for sharping the accuracy. To see the performance of proposed method, the 200 datasets are divided into data training and testing by using cross validation (k=10). The results show that the proposed method can predict the face image up to 70%. Moreover by adding normalization Z-Score, the accuration of prediction raise up to 89.5% (in average).

Integrating Fourier descriptors and PCA with neural networks for face recognition

Abstract: A new approach to the face recognition problem is presented through combining Fourier descriptors with principal component analysis (PCA) and neural networks. Here the faces are vertically oriented frontal view with scaling, orientation, expression, and illumination changes. There are many research activities on face recognition using the face space which is described by a set of eigenfaces. Each face is efficiently represented by its projection onto the space expanded by the eigenfaces and has a new descriptor. Previous work on eigenface has shown that it performs well only with changes in expression, but results are poor in the case of rotating, or scaling the input face. In order to enhance the performance of the eigenfaces technique to accommodate other variations of the input face, the Fourier vector of each face is projected in the eigenspace. Neural networks are used to recognize the face through learning the correct classification of these new descriptors. A real-time system has been created which combines the face detection and recognition techniques. A recognition rate of 91% has been achieved over real tests. It is also shown that our proposed system behaves accurately in the case of rotated or scaled faces as well as for changes in expression.

Face Recognition Based on Support Vector Method

Abstract: Support Vector Machines are a binary classification method and have demonstrated excellent results in pattern recognition. Face recognition is a multi-class problem, where the number of classes is of the known individuals. This paper we use face data extracted from Eigenfeatures and developed a method to extend SVM to using in multi-class. The training set consists of 5 images of each of the 50 persons equally distributed among frontal, approximately 15°rotated respectively, and the test set consists of 10 images each of the 50 persons. In the ICT-YCNC face gallery, the proposed system obtains competitive results highly: a correct recognition rate of 94.8% for all the 50 persons, to the less number of the persons and to the famous ORL face gallery we also get good face recognition rate.

Recognition of expression variant faces - a principle component analysis based approach for access control

Abstract: Machine automated face recognition has gained significant importance due to its scientific challenges and its potential applications. However, most of the systems designed to date can only successfully recognize faces when images are obtained under constrained conditions. The success of face recognition systems rely on a variety of information in images of human faces such as pose, facial expression, occlusion and presence or absence of structural components. The proposed model targets an approach for the recognition of expression variant faces since there are very few face recognition solutions to address this problem and this is a key research area in face recognition. This model proposes an approach to face recognition where the facial expression in the training image and in the testing image diverge and only a single sample image per class is available to the system. The input to the system is a frontal face image with neutral expression and identical background where the subjects' hair is tied away from the face. The proposed model is based on Principal Component Analysis approach. This approach has been applied on a set of images in order to extract a set of Eigen-images known as Eigen faces and weights of this representation are used for recognition. For the classification task, distance metric Euclidean Distance has been used to find the distance with the weight vectors associated with each of the training images. When tested with eight subjects and six basic expressions the overall recognition rate was 89%, for trained faces.