Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Trace Transform and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Image Identifier Based on Hausdorff Shape Trace Transform
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
A shape- and texture-based enhanced Fisher classifier for face 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
Towards face unlock: on the difficulty of reliably detecting faces on mobile phones
Proceedings of the 10th International Conference on Advances in Mobile Computing & Multimedia
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Face recognition research still face challenge in some specific domains such as pose, illumination and Expression. In this paper, we proposes a highly robust method for face recognition with variant illumination, scaling, rotation, blur, reflection and expression. Techniques introduced in this work are composed of two parts. The first one is the detection of facial features by using the concepts of Trace Transform and Fourier transform. Then, in the second part, the Hausdorff distance is employed to measure and determine of similarity between the models and tested images. Finally, our method is evaluated with experiments on the AR, ORL, Yale and XM2VTS face databases and compared with other related works (e.g. Eigen face and Hausdorff ARTMAP). The extensive experimental results show that the average of accuracy rate of face recognition with variant illumination, scaling, rotation, blur, reflection and difference emotions is higher than 88%.