NDLI: Training-based head pose estimation under monocular vision

Content Provider	IET Digital Library
Author	Guo, Zhizhi Zhou, Qianxiang Liu, Zhongqi Liu, Chunhui
Abstract	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.
Starting Page	798
Ending Page	805
Page Count	8
ISSN	17519632
Volume Number	10
e-ISSN	17519640
Issue Number	Issue 8, Dec (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/10/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0457
Journal	IET Computer Vision
Publisher Date	2016-05-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision Computer Vision And Image Processing Technique Facial Key Point Detection System Feature Extraction Feature Vector Space Head Pose Linear Combination Head Pose Space Image Recognition Minimisation Monocular Vision Optimisation Technique Pose Estimation Sparse Training Sample Set Selection Training Sample Number Reduction Training-based Head Pose Estimation ℓ1-minimisation
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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