Training-based head pose estimation under monocular vision

• Published in: IET computer vision Vol. 10; no. 8; pp. 798 - 805
• Main Authors: Guo, Zhizhi, Zhou, Qianxiang, Liu, Zhongqi, Liu, Chunhui
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.12.2016; Wiley
• Subjects: Accuracy; computer vision; Face recognition; Facial; facial key point detection system; feature extraction; feature vector space; Hardware; head pose linear combination; head pose space; Illumination; minimisation; Monocular vision; Parameters; pose estimation; Research Article; sparse training sample set selection; Training; training sample number reduction; training-based head pose estimation; ℓ1-minimisation; monocular vision; ℓ1-minimisation; head pose space; facial key point detection system; sparse training sample set selection; feature extraction; feature vector space; head pose linear combination; training-based head pose estimation; pose estimation; computer vision; minimisation; training sample number reduction
• ISSN: 1751-9632; 1751-9640; 1751-9640
• DOI: 10.1049/iet-cvi.2015.0457

Although many 3D head pose estimation methods based on monocular vision can achieve an accuracy of 5°, how to reduce the number of required training samples and how to not to use any hardware parameters as input features are still among the biggest challenges in the field of head pose estimation. To aim at these challenges, the authors propose an accurate head pose estimation method which can act as an extension to facial key point detection systems. The basic idea is to use the normalised distance between key points as input features, and to use ℓ1-minimisation to select a set of sparse training samples which can reflect the mapping relationship between the feature vector space and the head pose space. The linear combination of the head poses corresponding to these samples represents the head pose of the test sample. The experiment results show that the authors’ method can achieve an accuracy of 2.6° without any extra hardware parameters or information of the subject. In addition, in the case of large head movement and varying illumination, the authors’ method is still able to estimate the head pose.