Rotated haar-like features for face detection with in-plane rotation

Authors:
Shaoyi Du;Nanning Zheng;Qubo You;Yang Wu;Maojun Yuan;Jingjun Wu
Affiliations:
Institute of Artificial Intelligence and Robotics, Xi’an jiaotong University, Xi’an, Shaanxi Province, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an jiaotong University, Xi’an, Shaanxi Province, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an jiaotong University, Xi’an, Shaanxi Province, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an jiaotong University, Xi’an, Shaanxi Province, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an jiaotong University, Xi’an, Shaanxi Province, P.R. China;Institute of Artificial Intelligence and Robotics, Xi’an jiaotong University, Xi’an, Shaanxi Province, P.R. China
Venue:
VSMM'06 Proceedings of the 12th international conference on Interactive Technologies and Sociotechnical Systems
Year:
2006

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Abstract

This paper extends the upright face detection framework proposed by Viola et al. 2001 to handle in-plane rotated faces. These haar-like features work inefficiently on rotated faces, so this paper proposes a new set of ±26.565 ° haar-like features which can be calculated quickly to represent the features of rotated faces. Unlike previous face detection techniques in training quantities of samples to build different rotated detectors, with these new features, we address to build different rotated detectors by rotating an upright face detector directly so as to achieve in-plane rotated face detection. This approach is selected because of its computational efficiency, simplicity and training time saving. This proposed method is tested on CMU-MIT rotated test data and yields good results in accuracy and maintains speed advantage.