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Forehead-based face detection algorithm with multi-feature cascade framework for classroom
Author(s): Meng Guo; Qingmei Cheng; Bo Sun; Jun He
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Paper Abstract

Obviously, face recognition may be good for obtaining the students’ learning behaviors in class, which are useful for either teaching quality estimation or individualized teaching. While, exact face detection is the first and necessary task in such application. Considering the real setting of a classroom, it is also challenging. After careful studying, it is found that special position of the cameras in a classroom may lead various poses, and severe occlusion problem, which can also occur in other indoor surveillance-used places, such as large gatherings. In this paper, a forehead-based face detection model applied to such particular environments are proposed. The key idea is to obtain faces by detecting forehead area, which has a relatively high position and rich-information of shape, color and texture, instead of commonly used landmarks. The method consists of a post classifier based on extended Haar-Like feature, and a second classifier based on a color feature, called Multi-Channel-Color-Frequency Feature (MCCFF). To make it more efficient, we combine them in the same cascade framework. Practically, experiments on the database obtained from the real class, i.e. BNULSVED show that the proposed approach is effective and efficient.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108061N (9 August 2018); doi: 10.1117/12.2503057
Show Author Affiliations
Meng Guo, Beijing Normal Univ. (China)
Qingmei Cheng, Beijing Information Technology College (China)
Bo Sun, Beijing Normal Univ. (China)
Jun He, Beijing Normal Univ. (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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