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- W2597284493 abstract "There are a lot of actual application scenarios of head pose estimation, such as live detection, human interaction, gesture-based equipment, VR devices. This paper proposes a method based on linear regression to get the pose angle. The method utilizes supervised gradient descent method to get the facial feature points, and estimates the head pose by least square regression. In regression modeling, we only use seven feature point positions, which decrease the model size and the computing load. A data normalization technique is employed to eliminate the effect of the camera imaging parameters on pose estimation. Moreover, the training data set is derived from a group of 3D face models according to the camera imaging model, which can provide precise pose parameters. Experiments validate the proposed method, and show it can be run in real time. The algorithm can be easily transplanted to the mobile terminals. We have released our source code at https://github.com/RoboPai/sdm." @default.
- W2597284493 created "2017-03-23" @default.
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- W2597284493 date "2017-01-01" @default.
- W2597284493 modified "2023-09-27" @default.
- W2597284493 title "Real-Time Head Pose Estimation on Mobile Devices" @default.
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- W2597284493 doi "https://doi.org/10.1007/978-3-319-54407-6_41" @default.
- W2597284493 hasPublicationYear "2017" @default.
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