Abstract
The real-time face occlusion recognition is an important computer vision problem, especially for the public safety field. In order to construct a real-time face occlusion recognition system, this paper first established a large occlusion face database. Then, this paper proposed a face occlusion recognition algorithm based on the fusion of histogram of oriented gradient(HOG) and local binary pattern(LBP), the experimental results show that the occlusion face recall rate and the unobstructed face recall rate are 92.03% and 93.58% respectively, the speed is about 12.26 ms. Finally, taking into account time factor, this paper established a lightweight deep neural network based on AlexNet with an occlusion face recall rate and an unobstructed face recall rate of 91.79% and 91.42% respectively, and the speed is approximately 22.92 ms. The experimental results show that the face occlusion recognition method based on HOG+LBP features not only improves the recognition rate of occlusion face, but also reduces the time complexity, and illustrates the effectiveness of the algorithm.
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Acknowledgment
This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. N160504007)
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Zhang, X., Zheng, B., Li, Y., Yang, L. (2019). Real-Time Face Occlusion Recognition Algorithm Based on Feature Fusion. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_29
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DOI: https://doi.org/10.1007/978-3-030-31456-9_29
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