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Article

Driver Face Verification with Depth Maps

Softech-ICT, Dipartimento di Ingegneria Enzo Ferrari, Università degli studi di Modena e Reggio Emilia, 41125 Modena, Italy
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Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(15), 3361; https://doi.org/10.3390/s19153361
Received: 12 June 2019 / Revised: 25 July 2019 / Accepted: 26 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue Time of Flight (TOF) Cameras)
Face verification is the task of checking if two provided images contain the face of the same person or not. In this work, we propose a fully-convolutional Siamese architecture to tackle this task, achieving state-of-the-art results on three publicly-released datasets, namely Pandora, High-Resolution Range-based Face Database (HRRFaceD), and CurtinFaces. The proposed method takes depth maps as the input, since depth cameras have been proven to be more reliable in different illumination conditions. Thus, the system is able to work even in the case of the total or partial absence of external light sources, which is a key feature for automotive applications. From the algorithmic point of view, we propose a fully-convolutional architecture with a limited number of parameters, capable of dealing with the small amount of depth data available for training and able to run in real time even on a CPU and embedded boards. The experimental results show acceptable accuracy to allow exploitation in real-world applications with in-board cameras. Finally, exploiting the presence of faces occluded by various head garments and extreme head poses available in the Pandora dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method. View Full-Text
Keywords: driver face verification; depth maps; fully-convolutional network; Siamese model; deep learning; automotive driver face verification; depth maps; fully-convolutional network; Siamese model; deep learning; automotive
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MDPI and ACS Style

Borghi, G.; Pini, S.; Vezzani, R.; Cucchiara, R. Driver Face Verification with Depth Maps. Sensors 2019, 19, 3361. https://doi.org/10.3390/s19153361

AMA Style

Borghi G, Pini S, Vezzani R, Cucchiara R. Driver Face Verification with Depth Maps. Sensors. 2019; 19(15):3361. https://doi.org/10.3390/s19153361

Chicago/Turabian Style

Borghi, Guido, Stefano Pini, Roberto Vezzani, and Rita Cucchiara. 2019. "Driver Face Verification with Depth Maps" Sensors 19, no. 15: 3361. https://doi.org/10.3390/s19153361

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