Shape Initialization Without Ground Truth For Face Alignment

	Shape Initialization Without Ground Truth For Face Alignment
	Qin, Rizhen; Zhang, Ting
	2016-03
会议名称	2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
会议录名称	2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
会议日期	20-25 March 2016
会议地点	Shanghai, China
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.
关键词	Shape Initialization Face Alignment Dpm Multiple Initial Shapes Structured Svm
收录类别	EI
语种	英语
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/11493
专题	多模态人工智能系统全国重点实验室_机器人视觉
通讯作者	Qin, Rizhen
作者单位	Institute of Automation, Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Qin, Rizhen,Zhang, Ting. Shape Initialization Without Ground Truth For Face Alignment[C],2016.

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