A Highly Robust Approach Face Recognition Using Hausdorff-Trace Transformation

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Abstract

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%.