戴眼镜也能识别人脸

A. Liang, Chathurdara Sri Nadith Pathirage, Chenyu Wang, Wanquan Liu, Ling Li, J. Duan
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引用次数: 2

摘要

在本文中,我们解决了对戴眼镜的人脸进行人脸识别的挑战。这是机场人脸识别和自动身份检查的一个常见问题,因为乘客在通过海关时经常忘记摘下眼镜。为了解决这一问题,我们首先提出了一种基于树形结构人脸检测模型的眼镜存在自动检测模型,该模型可以检测眼镜是否存在,并在正面人脸的边缘、铰链和桥上分配标志。实验结果表明,该方法对各种人脸数据库的检测率都非常满意。其次,基于玻璃上的地标,采用非局部颜色全变差(CTV)方法进行玻璃的去除;同时,我们运用深度学习技术,将镜片上的眼镜痕迹和光反射视为噪声,进一步去除它们。最后,采用几种典型方法对摘眼镜后的人脸识别进行了实验,实验结果表明,我们的摘眼镜框架能够显著提高人脸识别的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face Recognition Despite Wearing Glasses
In this paper we address the challenge of performing face recognition on human faces that are wearing glasses. This is a common problem for face recognition and automatic identity checking at airports, as passengers frequently forget to remove their glasses when passing through customs. In order to solve this problem, we first propose an automatic glasses presence detection model based on the tree-pictorial-structured face detection model and such model can detect the presence of glasses and further assign landmarks on the rim, hinge, and bridge of the glasses on frontal faces. Experimental results show that the glasses detection rate is highly satisfactory for various face databases. Secondly, based on the landmarks on glasses, we apply the non-local colour total variation (CTV) inpainting approach in an attempt to remove the glasses; also, we apply the deep learning technique to further remove the traces of glasses and light reflection on lenses by regarding them as noises. Finally, experiments for face recognition after glasses removal are conducted by using some typical approaches and the results show that our glasses removal framework can improve face recognition accuracy significantly.
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