Face alignment by robust discriminative Hough voting
作者：
Highlights：
• Extending the range of traditional CLM framework by including the nonparametric (exemplar-based) shape models.
• Incorporating the prior knowledge of very few anchor points into the task of face alignment, while being robust against localization errors of anchor points.
• Exploiting the power of discriminative learning under the generative exemplar-based CLM.
• Promising results on four challenging databases, i.e., LFW, LFPW, HELEN and IBUG.
• Exploring the deep CNN technique in the RANSAC step to further boost the performance of the proposed system.
摘要
Highlights•Extending the range of traditional CLM framework by including the nonparametric (exemplar-based) shape models.•Incorporating the prior knowledge of very few anchor points into the task of face alignment, while being robust against localization errors of anchor points.•Exploiting the power of discriminative learning under the generative exemplar-based CLM.•Promising results on four challenging databases, i.e., LFW, LFPW, HELEN and IBUG.•Exploring the deep CNN technique in the RANSAC step to further boost the performance of the proposed system.
论文关键词：Face alignment,Hough voting,Constrained Local Models
论文评审过程：Received 9 November 2015, Revised 8 April 2016, Accepted 7 May 2016, Available online 24 May 2016, Version of Record 11 June 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.05.017
