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[Domestic Journal] Multi-Scale, Multi-Object and Real-Time Face Detection and Head Pose Estimation Using Deep Neural Networks
Journal of Korea Robotics Society , September 2017
  [09_313-321]KRS17-015.pdf [09_313-321]KRS17-015.pdf (3.8M) [297]
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.


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