Abstract
Face recognition has a wide range of applications from education to the industries. Existing face recognition systems were designed by using the RGB input only. Therefore, this research proposes a new system to increase the performance of the face recognition system by fusing the benefit of RGB, HSV, and raw RGB features. First, the HSV image was computed from the raw RGB input. Second, the LBP image was calculated from the draw RGB input. Third, the LBP, HSV, and RGB features are combined to produce a new feature. Fourth the proposed feature is then input to the deep learning-based FaceNet model to find the location of the human face. Finally, the support vector machine is used to identify the human name. Experimental results prove that our proposed system achieves high accurate detection rate. The proposed method improves the accuracy of existing face recognition systems by 2.15% in terms of the detection rate under our dataset and the Kaggle dataset. The proposed method obtained the detection rate of 96.25% on the custom dataset.
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The authors would like to express gratitude to Eastern International University (EIU) Vietnam.
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Nguyen, Q.N., Debnath, N.C., Nguyen, V.D. (2023). Face Recognition Based on Deep Learning and HSV Color Space. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_15
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DOI: https://doi.org/10.1007/978-3-031-20601-6_15
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