Borghi Guido, Pini Stefano, Vezzani Roberto, Cucchiara Rita
Softech-ICT, Dipartimento di Ingegneria Enzo Ferrari, Università degli studi di Modena e Reggio Emilia, 41125 Modena, Italy.
Sensors (Basel). 2019 Jul 31;19(15):3361. doi: 10.3390/s19153361.
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 , (HRRFaceD), and . 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 dataset, we successfully test the proposed system also during strong visual occlusions. The excellent results obtained confirm the efficacy of the proposed method.
面部验证是检查两张提供的图像是否包含同一个人的面部的任务。在这项工作中，我们提出了一种全卷积暹罗架构来处理这项任务，在三个公开数据集上取得了领先的结果，即 、 （HRRFaceD）和 。所提出的方法将深度图作为输入，因为深度相机已被证明在不同光照条件下更可靠。因此，即使在完全或部分没有外部光源的情况下，该系统也能够工作，这是汽车应用的一个关键特性。从算法角度来看，我们提出了一种参数数量有限的全卷积架构，能够处理可用于训练的少量深度数据，甚至在CPU和嵌入式板上也能实时运行。实验结果表明，其准确率足以在车载摄像头的实际应用中使用。最后，利用 数据集中存在的被各种头部服饰遮挡的面部以及极端头部姿势，我们还成功地在强视觉遮挡情况下测试了所提出的系统。所获得的优异结果证实了所提方法的有效性。
