다중크기와 다중객체의 실시간 얼굴 검출과머리 자세 추정을 위한 심층 신경망 Multi-Scale, Multi-Object and Real-Time Face Detection and Head Pose Estimation Using Deep Neural Networks

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One of the most frequently performed tasks in human-robot interaction (HRI), intelligent vehicles, and security systems is face related applications such as face recognition, facial expression recognition, driver state monitoring, and gaze estimation. In these applications, accurate head pose estimation is an important issue. However, conventional methods have been lacking in accuracy, robustness or processing speed in practical use. In this paper, we propose a novel method for estimating head pose with a monocular camera. The proposed algorithm is based on a deep neural network for multi-task learning using a small grayscale image. This network jointly detects multi-view faces and estimates head pose in hard environmental conditions such as illumination change and large pose change. The proposed framework quantitatively and qualitatively outperforms the state-of-the-art method with an average head pose mean error of less than 4.5° in real-time.
Publisher
한국로봇학회
Issue Date
2017-09
Language
Korean
Keywords

Head Pose; Deep Learning; Convolutional Neural Network

Citation

로봇학회 논문지, v.12, no.3, pp.313 - 321

ISSN
1975-6291
DOI
10.7746/jkros.2017.12.3.313
URI
http://hdl.handle.net/10203/225989
Appears in Collection
EE-Journal Papers(저널논문)
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