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Conference Papers Year : 2018

Combined and Weighted Features for Robust Multispectral Face Recognition


Face recognition has been very popular in recent years, for its advantages such as acceptance by the wide public and the price of cameras, which became more accessible. The majority of the current facial biometric systems use the visible spectrum, which suffers from some limitations, such as sensitivity to light changing, pose and facial expressions. The infrared spectrum is more relevant to facial biometric, for its advantages such as robustness to illumination change. In this paper, we propose two multispectral face recognition approaches that use both the visible and infrared spectra. We tested the new approaches with Uniform Local Binary Pattern (uLBP) as a local descriptor and Zernike Moments as a global descriptor on IRIS Thermal/Visible and CSIST Lab 2 databases. The experimental results clearly demonstrate the effectiveness of our multispectral face recognition system compared to a system that uses a single spectrum.
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Dates and versions

hal-01913880 , version 1 (06-11-2018)





Nadir Kamel Benamara, Ehlem Zigh, Tarik Boudghene Stambouli, Mokhtar Keche. Combined and Weighted Features for Robust Multispectral Face Recognition. 6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA), May 2018, Oran, Algeria. pp.549-560, ⟨10.1007/978-3-319-89743-1_47⟩. ⟨hal-01913880⟩
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