计算机科学
面子(社会学概念)
人工智能
任务(项目管理)
可靠性(半导体)
计算机视觉
领域(数学)
卷积神经网络
深度学习
面部识别系统
网络体系结构
模式识别(心理学)
工程类
量子力学
物理
社会学
计算机安全
功率(物理)
系统工程
纯数学
社会科学
数学
作者
Qi Wang,Hang Lei,Xupeng Wang
标识
DOI:10.1109/icites53477.2021.9637086
摘要
Face verification is one of the most researched and demanding tasks in the field of computer vision. The task of Face verification is to check whether two input faces are the same object. With the development of depth cameras and their reliability against light changes, face verification has become more widely used in many scenarios, for example night driving. This paper constructs a deep Siamese architecture for face verification based on two same fully convolutional networks, relying on depth images. Despite the lack of deep-oriented depth-based datasets, the network only relies on the small amount of depth data available on Pandora dataset for training and still achieves state-of-art results and real time performance, and the network also get excellent results during the variation of head pose.
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