A local space rotation invariant feature extraction method for facial interest points detection

Kuo Chen; Xibin Jia; Runyuan Wang

doi:10.1117/12.2243857

29 August 2016 A local space rotation invariant feature extraction method for facial interest points detection

Kuo Chen, Xibin Jia, Runyuan Wang

Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 1003306 (2016) https://doi.org/10.1117/12.2243857
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China

Abstract

Fast and reliable facial interest point detection is critical basis in intelligent human machine interaction to understand human behavior. Considering the depth data’s outstanding advantage on robustness of complex background and illumination variation, we address the problem of facial interest point's detection based on depth images rather than normal intensity images to locate points with salient depth discriminable characteristic. In this paper, we propose to extract Haar-like features from facial depth data for further classification of interested point detection. To alleviate the influence of head rotation, a novel local space rotation invariant (LSRI) feature extraction method is presented in the paper by adjusting the depth image with estimated rotation angles. In our experiments, we select 6 kinds of templates to extract the features and use algorithms including Adaboost, Random Forests, J48(a decision tree algorithm)as classifiers respectively to realize the interest point location. The experiment results show that our algorithm has high point location accuracy rate at 96.1%. The proposed LSRI feature outperforms the Haar-like feature in depth data without doing local posture adjustment.

Citation Download Citation

Kuo Chen, Xibin Jia, and Runyuan Wang "A local space rotation invariant feature extraction method for facial interest points detection", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 1003306 (29 August 2016); https://doi.org/10.1117/12.2243857

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KEYWORDS

Head

Feature extraction

Nose

Detection and tracking algorithms

Eye

Image analysis

Mouth

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