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Toward a Robust Shape and Texture Face Descriptor for Efficient Face Recognition in the Wild

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Computer Analysis of Images and Patterns (CAIP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13053))

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Abstract

Face recognition in complex environments has attracted the attention of the research community in the last few years due to the huge difficulties that can be found in images captured in such environments. In this context, we propose to extract a robust facial description in order to improve facial recognition rate even in the presence of illumination, pose or facial expression problems. Our method uses texture descriptors, namely Mesh-LBP extracted from 3D Meshs. These extracted descriptors will then be used to train a Convolution Neural Networks (CNN) to classify facial images. Experiments on several datasets has shown that the proposed method gives promising results in terms of face recognition accuracy under pose, face expressions and illumination variation.

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Correspondence to Rahma Abed .

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Abed, R., Bahroun, S., Zagrouba, E. (2021). Toward a Robust Shape and Texture Face Descriptor for Efficient Face Recognition in the Wild. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_29

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  • DOI: https://doi.org/10.1007/978-3-030-89131-2_29

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