Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition System Using Local Autocorrelations and Multiscale Integration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Memory-Based Face Recognition for Visitor Identification
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Design of Radial Basis Function Network as Classifier in Face Recognition Using Eigenfaces
SBRN '98 Proceedings of the Vth Brazilian Symposium on Neural Networks
Fast learning in networks of locally-tuned processing units
Neural Computation
Matching pursuit filters applied to face identification
IEEE Transactions on Image Processing
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Face recognition using immune network based on principal component analysis
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Face Recognition Using Posterior Distance Model Based Radial Basis Function Neural Networks
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
PCA based immune networks for human face recognition
Applied Soft Computing
Radial basis function networks with hybrid learning for system identification with outliers
Applied Soft Computing
Self-adaptive RBF neural networks for face recognition
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
An improved hybrid approach to face recognition by fusing local and global discriminant features
International Journal of Biometrics
Expert Systems with Applications: An International Journal
Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks
International Journal of Mobile Computing and Multimedia Communications
International Journal of High Performance Systems Architecture
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In this paper, a face recognition technique using a radial basis function neural network (RBFNN) is presented. The centers of the hidden layer units of the RBFNN are selected by using a heuristic approach and point symmetry distance as similarity measure. The performance of the present method has been evaluated using the AT&T Laboratories Cambridge database (formerly called ORL face database) and compared with some other methods, which use the same database. The evaluation has been done using two methodologies; first with no rejection criteria, and then with rejection criteria. The experimental results show that the present method achieves excellent performance, both in terms of recognition rates and learning efficiency. The average recognition rates, as obtained using 10 different permutations of 1, 3 and 5 training images per subject are 76.06, 92.61 and 97.20%, respectively, when tested without any rejection criteria. On the other hand, by imposing rejection criteria, the average recognition rates of the system become 99.34, 99.80 and 99.93%, respectively, for the above permutations of the training images. The system recognizes a face within about 22ms on a low-cost computing system with a 450MHz P-III processor, and thereby extending its capability to identify faces in interframe periods of video and in real time.