NDLI: Robust multi-view face tracking

Content Provider	IEEE Xplore Digital Library
Author	Kwang Ho An Dong Hyun Yoo Sung Uk Jung Myung Jin Chung
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. Eng. & Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea (Kwang Ho An; Dong Hyun Yoo; Sung Uk Jung; Myung Jin Chung)
Abstract	For face tracking in a video sequence, various face tracking algorithms have been proposed. However, most of them have a difficulty in finding the initial position and size of a face automatically. In this paper, we present a fast and robust method for fully automatic multi-view face detection and tracking. Using a small number of critical rectangle features selected and trained by Adaboost learning algorithm, we can detect the initial position, size and view of a face correctly. Once a face is reliably detected, we can extract face and upper body color distribution from the detected facial regions and upper body regions for building a robust color modeling respectively. Simultaneously, each color modeling is performed by using k-means clustering and multiple Gaussian models. Then, fast and efficient multi-view face tracking is executed by using several critical features and a simple linear Kalman filter. Our proposed algorithm is robust to rotation, partial occlusions, and scale changes in front of dynamic, unstructured background. In addition, our proposed method is computationally efficient. Therefore, it can be executed in real-time.
Starting Page	1905
Ending Page	1910
File Size	298295
Page Count	6
File Format	PDF
ISBN	0780389123
DOI	10.1109/IROS.2005.1545533
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-02
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robustness Face detection Skin Humans Clustering algorithms Head Computer science Video sequences Body regions Application software Kalman filter Adaboost learning algorithm K-means clustering Multiple gaussian models Multi-view face tracking
Content Type	Text
Resource Type	Article

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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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