Unconstrained face alignment via cascaded compositional learning
Refereed conference paper presented and published in conference proceedings


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摘要We present a practical approach to address the problem of unconstrained face alignment for a single image. In our unconstrained problem, we need to deal with large shape and appearance variations under extreme head poses and rich shape deformation. To equip cascaded regressors with the capability to handle global shape variation and irregular appearance-shape relation in the unconstrained scenario, we partition the optimisation space into multiple domains of homogeneous descent, and predict a shape as a composition of estimations from multiple domain-specific regressors. With a specially formulated learning objective and a novel tree splitting function, our approach is capable of estimating a robust and meaningful composition. In addition to achieving state-of-the-art accuracy over existing approaches, our framework is also an efficient solution (350 FPS), thanks to the on-the-fly domain exclusion mechanism and the capability of leveraging the fast pixel feature.
著者Zhu S., Li C., Loy C.C., Tang X.
會議名稱2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
會議開始日26.06.2016
會議完結日01.07.2016
會議地點Las Vegas
會議國家/地區美國
詳細描述organized by IEEE,
出版年份2016
月份1
日期1
卷號2016-January
頁次3409 - 3417
國際標準書號9781467388511
國際標準期刊號1063-6919
語言英式英語

上次更新時間 2020-31-07 於 23:12