29 August 2016 High-quality initial shape estimation for cascade shape regression
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Proceedings Volume 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016); 100330Y (2016) https://doi.org/10.1117/12.2245134
Event: Eighth International Conference on Digital Image Processing (ICDIP 2016), 2016, Chengu, China
Abstract
Cascade shape regression has been proven to be an accurate, robust and fast framework for face alignment. Recently, a lot of methods based on this framework have emerged which focus on boosting learning method or extracting geometric invariant features. Despite the great success of these methods, none of them are initialization independent, which limits their prediction performance to some complex face shapes. In this paper, we propose a novel initialization scheme called high-quality initial shape estimation to generate high-quality initial face shapes. First, we extract Gabor features to represent facial appearance. Then we minimize the square error between the target shapes and the estimated initial shapes using a random regression forest and binary comparison features. Finally, we use a standard cascade shape regressor to regress the estimated initial shape for robust face alignment. Experimental results show that our method achieves state-of-the-art performance on the 300-W dataset, which is the most challenging dataset today.
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Kai Wu, Hengliang Zhu, Yangyang Hao, Lizhuang Ma, "High-quality initial shape estimation for cascade shape regression", Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330Y (29 August 2016); doi: 10.1117/12.2245134; https://doi.org/10.1117/12.2245134
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