Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (5): 1459-1465.DOI: 10.11772/j.issn.1001-9081.2018102057

• Virtual reality and multimedia computing • Previous Articles     Next Articles

Adaptive window regression method for face feature point positioning

WEI Jiawang, WANG Xiao, YUAN Yubo   

  1. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  • Received:2018-10-11 Revised:2018-12-16 Online:2019-05-14 Published:2019-05-10
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61001200), the Scientific Research Project of Shanghai (17DZ1101003).


魏嘉旺, 王肖, 袁玉波   

  1. 华东理工大学 信息科学与工程学院, 上海 200237
  • 通讯作者: 袁玉波
  • 作者简介:魏嘉旺(1995-),男,辽宁锦州人,硕士研究生,主要研究方向:数据挖掘、计算机视觉;王肖(1995-),女,甘肃天水人,硕士研究生,主要研究方向:数据挖掘、机器学习;袁玉波(1976-),男,云南宣威人,副教授,博士,主要研究方向:机器学习、数据科学、数据质量评估、数据挖掘。
  • 基金资助:

Abstract: Focused on the low positioning accuracy of Explicit Shape Regression (ESR) for some facical occlusion and excessive facial expression samples, an adaptive window regression method was proposed. Firstly, the priori information was used to generate an accurate face area box for each image, feature mapping of faces was performed by using the center point of the face area box, and similar transformation was performed to obtain multiple initial shapes. Secondly, an adaptive window adjustment strategy was given, in which the feature window size was adaptively adjusted based on the mean square error of the previous regression. Finally, based on the feature selection strategy of Mutual Information (MI), a new correlation calculation method was proposed, and the most relevant features were selected in the candidate pixel set. On the three public datasets LFPW, HELEN and COFW, the positioning accuracy of the proposed method is increased by 7.52%, 5.72% and 5.89% respectively compared to ESR algorithm. The experimental results show that the adaptive window regression method can effectively improve the positioning accuracy of face feature points.

Key words: Explicit Shape Regression (ESR), face feature point position, similar face transformation, adaptive window regression, Mutual Information (MI)

摘要: 针对显式形状回归(ESR)对于一些面部遮挡、面部表情过大样本定位精度低的问题,提出一种自适应窗回归方法。首先,应用先验信息为每张图片生成精确的人脸框,用人脸框的中心点对人脸进行特征映射,并进行相似变换得到多个初始形状;其次,提出一种自适应窗口调整策略,基于先前回归的均方误差自适应地调整特征窗口大小;最后,基于互信息(MI)的特征选择策略,提出新的相关性计算方法,在候选像素集中选出最相关的特征。在三个公开数据集LFPW、HELEN、COFW上,相较于ESR算法,所提方法的定位精度分别提升7.52%、5.72%和5.89%。实验结果表明,自适应窗回归方法可以有效提高人脸特征点定位精度。

关键词: 显式形状回归, 人脸特征点定位, 相似人脸变换, 自适应窗回归, 互信息

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