Open AccessA Fast and Accurate Face Tracking Scheme by using Depth Information in Addition to Texture Information
Author(s) -
Dong-Wook Kim,
Woo-Youl Kim,
Ji-Sang Yoo,
Young-Ho Seo
Publication year - 2014
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.226
H-Index - 27
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2014.9.2.707
Subject(s) - facial motion capture , computer science , tracking (education) , face (sociological concept) , artificial intelligence , computer vision , scheme (mathematics) , adaboost , matching (statistics) , face detection , track (disk drive) , spiral (railway) , object class detection , facial recognition system , process (computing) , pattern recognition (psychology) , mathematics , psychology , mathematical analysis , pedagogy , social science , statistics , sociology , classifier (uml) , operating system
This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.