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.
조회 수 304 댓글 0
|저 자||Byungtae Ahn, Dong-Geol Choi, In So Kweon|
|학 회||Journal of Korea Robotics Society|
Prev Intelligent Assistant for People with Low Vision Abilities Intelligent Assistant for People with Low Vision Abilities 2017.09.18by VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition Next VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition 2017.08.10by 이석주