Tan Shuqiu, Chen Dongyi, Guo Chenggang, Huang Zhiqi
School of Automation Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China.
Comput Intell Neurosci. 2017;2017:4579398. doi: 10.1155/2017/4579398. Epub 2017 Feb 19.
Facial feature point detection has been receiving great research advances in recent years. Numerous methods have been developed and applied in practical face analysis systems. However, it is still a quite challenging task because of the large variability in expression and gestures and the existence of occlusions in real-world photo shoot. In this paper, we present a robust sparse reconstruction method for the face alignment problems. Instead of a direct regression between the feature space and the shape space, the concept of shape increment reconstruction is introduced. Moreover, a set of coupled overcomplete dictionaries termed the shape increment dictionary and the local appearance dictionary are learned in a regressive manner to select robust features and fit shape increments. Additionally, to make the learned model more generalized, we select the best matched parameter set through extensive validation tests. Experimental results on three public datasets demonstrate that the proposed method achieves a better robustness over the state-of-the-art methods.
近年来，面部特征点检测取得了重大研究进展。众多方法已被开发并应用于实际的面部分析系统中。然而，由于表情和手势的巨大变化以及现实世界照片拍摄中存在遮挡，这仍然是一项极具挑战性的任务。在本文中，我们提出了一种用于面部对齐问题的鲁棒稀疏重建方法。引入了形状增量重建的概念，而不是在特征空间和形状空间之间进行直接回归。此外，以回归方式学习了一组称为形状增量字典和局部外观字典的耦合过完备字典，以选择鲁棒特征并拟合形状增量。此外，为了使学习到的模型更具通用性，我们通过广泛的验证测试选择最佳匹配参数集。在三个公共数据集上的实验结果表明，所提出的方法比现有方法具有更好的鲁棒性。
