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Face perception in videos : contributions to a visual saliency model and its implementation on GPUs

Abstract : Studies conducted in this thesis focuses on faces and visual attention. We are interested to better understand the influence and perception of faces, to propose a visual saliency model with face features. Throughout the thesis, we concentrate on the question, "How people explore dynamic visual scenes, how the different visual features are modeled to mimic the eye movements of people, in particular, what is the influence of faces?" To answer these questions we analyze the influence of faces on gaze during free-viewing of videos, as well as the effects of the number, location and size of faces. Based on the findings of this work, we propose model with face as an important information feature extracted in parallel alongside other classical visual features (static and dynamic features). Finally, we propose a multi-GPU implementation of the visual saliency model, demonstrating an enormous speedup of more than 132 times compared to a multithreaded CPU.
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https://tel.archives-ouvertes.fr/tel-00923796
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Anis Ur Rahman. Face perception in videos : contributions to a visual saliency model and its implementation on GPUs. Signal and Image processing. Université de Grenoble, 2013. English. ⟨NNT : 2013GRENT102⟩. ⟨tel-00923796v2⟩

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