Abstract
Although 2D facial landmark detection methods built on the cascaded regression framework have been widely researched, their performance was still limited by face shape deformations and poor light conditions. With the assist of extra shape information provided by 3D facial model, these difficulties can be eased to some degree. In this paper, we propose 3D Cascaded Regression for detecting facial landmarks on 3D faces. Our algorithm makes full use of both texture and depth information to overcome the difficulties caused by expression variations, and generates shape increments based on a weighted mixture of two separated shape updates regressed from texture and depth, respectively. Finally, the shape estimation is mapped into the original 3D facial data to obtain three-dimensional landmark coordinates. Experimental results on the BU-4DFE database demonstrate that our proposed approach achieves satisfactory performance in terms of detection accuracy and robustness, significantly superior to state-of-the-art method.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (No. 61202161) and the National Key Scientific Instrument and Equipment Development Projects of China (No. 2013YQ49087904).
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Xu, J., Zhao, Q. (2016). Cascaded Regression for 3D Face Alignment. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_9
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DOI: https://doi.org/10.1007/978-3-319-46654-5_9
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