3D face detection based on salient features extraction and skin colour detection using data mining |
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| Authors: | Abdul Rahman El Sayed Hassan Alabboud Adnan Yassine |
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| Affiliation: | 1. Mathematics Department, Normandie University, UNIHAVRE, LMAH, Le Havre, France;2. Business and Administration Faculty, Lebanese University, Tripoli, Lebanon;3. Institut Supérieur d'Etudes Logistiques, Université du Havre, Le Havre, France |
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| Abstract: | Face detection has an essential role in many applications. In this paper, we propose an efficient and robust method for face detection on a 3D point cloud represented by a weighted graph. This method classifies graph vertices as skin and non-skin regions based on a data mining predictive model. Then, the saliency degree of vertices is computed to identify the possible candidate face features. Finally, the matching between non-skin regions representing eyes, mouth and eyebrows and salient regions is done by detecting collisions between polytopes, representing these two regions. This method extracts faces from situations where pose variation and change of expressions can be found. The robustness is showed through different experimental results. Moreover, we study the stability of our method according to noise. Furthermore, we show that our method deals with 2D images. |
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| Keywords: | 3D facial point clouds detection 3D point clouds saliency 3D point clouds segmentation classification 3D object processing points clouds |
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