Abstract
Scientists have attempted to enhance face recognition accuracy by using 3D images to solve pose variability, lighting issues, and occlusion; but the applicability of these approaches is insufficient in realistic applications due to the slowness and high cost of 3D sensors. In this paper, we developed a face recognition process for low-cost and low-quality Microsoft Kinect sensor based on the Histogram of Oriented Gradient (HOG) descriptor. After locating the nose tip, the face is extracted and filtered to reduce the noise, the feature vectors are extracted by first computing the shape index map and then applying the HOG descriptor. For identification, the Collaborative Representation Classifier (CRC) is used. We compare our approach results with previous ones obtained on the publicly available CurtinFaces database, achieving rank one recognition rates of 99.87% and 82.53% when testing against expression and pose variations respectively.
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Boumedine, A.Y., Bentaieb, S., Ouamri, A., Mallek, A. (2021). 3D Face Identification Using HOG Features and Collaborative Representation. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_1
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DOI: https://doi.org/10.1007/978-3-030-69418-0_1
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