Abstract
With quick development of Kinect, depth image has become an important channel for assisting the color/infrared image in diverse computer vision tasks. Kinect can provide depth image as well as color and infrared images, which are suitable for multi-model vision tasks. This paper presents a framework for intensity-depth face alignment based on cascade shape regression. Information from intensity and depth images is combined during feature selection in cascade shape regression. Experimental results show that this combination improves face alignment accuracy notably.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (Grant No. 61272248), the National Basic Research Program of China (Grant No. 2013CB329401), and the Science and Technology Commission of Shanghai Municipality (Grant No. 13511500200).
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Cao, Y., Lu, BL. (2015). Intensity-Depth Face Alignment Using Cascade Shape Regression. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9492. Springer, Cham. https://doi.org/10.1007/978-3-319-26561-2_27
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DOI: https://doi.org/10.1007/978-3-319-26561-2_27
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