Shape Initialization Without Ground Truth For Face Alignment

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	Shape Initialization Without Ground Truth For Face Alignment
	Qin, Rizhen; Zhang, Ting
	2016-03
会议日期	20-25 March 2016
会议地点	Shanghai, China
关键词	Shape Initialization Face Alignment Dpm Multiple Initial Shapes Structured Svm
英文摘要	The shape initialization is a crucial step for face alignment. In the literature, many approaches use the ground truth points to compute the bounding box. However, it is not always possible to detect an accurate bounding box in real applications due to various adverse factors. In this work, an effective initialization approach for face alignment is proposed. Firstly a modified Deformable Part Models (DPM) is used to estimate the face pose and the bounding box to obtain an initial shape. Then by detecting the two pupils, the roll rotation of the face is measured to correct the initial shape. To further increase the robustness and accuracy of face alignment, multiple initial shapes for each face are generated, then each one is refined by a cascade regression-based approach and we can get multiple shape estimations. Finally a better final shape is obtained by fusing the multiple estimations via the structured SVM learning. Experiments on challenging datasets and comparison with the state-of-the-art methods validate our proposed method in unconstrained environment.
会议录	2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/11493]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
通讯作者	Qin, Rizhen
作者单位	Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Qin, Rizhen,Zhang, Ting. Shape Initialization Without Ground Truth For Face Alignment[C]. 见:. Shanghai, China. 20-25 March 2016.