Abstract
With the rise of crimes associated with ATM, face occlusion detection has gained more and more attention because it facilitates the surveillance system of ATM to enhance the safety by pinpointing disguised among customers and giving alarms when suspicious customer is found. Inspired by strong learning ability of deep learning from data and high efficient feature representation ability, this paper proposes a cascaded Convolutional Neural Network (CNN) based face occlusion detection method. In the proposed method, three cascaded CNNs are used to detect head, eye occlusion and mouth occlusion. Experimental results show that the proposed method is very effective on two test datasets.
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Acknowledgement
The project was supported by the Science and Technology Planning Project of Hebei Province, China (No. 15210124), and the Science and Technology Research Project of Higher School in Hebei Province, China (No. Z2015105).
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Zhang, Y., Lu, Y., Wu, H., Wen, C., Ge, C. (2016). Face Occlusion Detection Using Cascaded Convolutional Neural Network. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_79
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DOI: https://doi.org/10.1007/978-3-319-46654-5_79
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