Looking at People: Sensing for Ubiquitous and Wearable Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Face Recognition through Geometrical Features
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Partial Face Recognition Using Radial Basis Function Networks
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Robust Face Recognition under Lighting Variations
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Journal of Cognitive Neuroscience
Multiresolution face recognition
Image and Vision Computing
Automatic 3D reconstruction for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
Face recognition/detection by probabilistic decision-based neural network
IEEE Transactions on Neural Networks
Complex cell prototype representation for face recognition
IEEE Transactions on Neural Networks
Improving the interest operator for face recognition
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
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Previous work has shown that Gabor feature extraction is one of the most effective techniques employed for the human face recognition problem. However, the selection of a particular set of Gabor filters is often problematic and, also the computational requirements are considerable. We propose an alternative feature extraction method - the Interest Operator - to be applied for the facial recognition problem. This method has already been successfully used in the mobile robots navigation, stereoscopic vision and automatic target recognition. Experimental results presented in this paper indicate that classifiers, both neural (Multi-layer Perceptron) and statistical (k Nearest Neighbour), using the Interest Operator - based feature extraction, are capable to achieve almost the same classification rate as the Gabor-wavelet-based methods but in one order of magnitude lower processing time. A special care has been put on the selection of the feature extraction filters and classifiers parameters. Then, on AT&T public facial database, the system has achieved an average recognition rate of 95.2% using Gabor Approach and 94.7% using the Interest Operator.