[1]闫保中,何伟,韩旭东.基于表观模型的人脸特征点提取[J].应用科技,2020,47(6):47-52.[doi:10.11991/yykj.202001006]
　YAN Baozhong,HE Wei,HAN Xudong.Facial feature point extraction based on active appearance model[J].Applied science and technology,2020,47(6):47-52.[doi:10.11991/yykj.202001006]
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年6期
页码:
47-52
栏目:
智能科学与技术
出版日期:
2021-01-31

文章信息/Info

Title:
Facial feature point extraction based on active appearance model
作者:
闫保中;  何伟;  韩旭东
哈尔滨工程大学 自动化学院，黑龙江 哈尔滨 150001
Author(s):
YAN Baozhong;  HE Wei;  HAN Xudong
College of Automation, Harbin Engineering University, Harbin 150001, China
关键词:
表观模型; 特征提取; 人脸特征点; 主动表观模型; 非法变形; 方向梯度直方图特征; 拟合; 头部姿态
Keywords:
appearance model; feature extraction; facial feature points; active appearance model (AAM); illegal deformation; histogram of oriented gradient(HOG)characteristics; fitting; head pose
分类号:
TP31
DOI:
10.11991/yykj.202001006
文献标志码:
A
摘要:
针对在人脸特征点提取过程中模型预估能力不足，拟合过程中的非法形变导致的特征点提取速度慢和提取精度不高的问题，本文提出了一种基于主动表观模型(AAM)的人脸特征点提取算法的改进算法。将头部作初始分类，不同的头部姿态选取不同人脸模型进行拟合，这样能避免初始化模型与真实特征点位置相差过大，从而使得模型能更快的收敛，提高特征点提取速度。同时提出一种方法，对拟合过程中的形状变量加以限制，能有效过滤掉不满足人脸形状的特征点模型，防止拟合过程中的非法形变，使提取的人脸特征点更接近真实位置。实验结果表明，改进的基于AAM的人脸特征点提取算法在时间效率和准确率上都所有提高。
Abstract:
In order to solve the problems of slow speed and low accuracy in the process of facial feature point extraction that are caused by insufficient model prediction ability and illegal deformation in the fitting process, an improved algorithm of facial feature point extraction is proposed in this paper based on active appearance model (AAM). The initial classification of head is carried out, and different head pose is fitted by different facial model. In this way, the difference of position between the initial model and the real feature points can be avoided so that the model can converge faster and the feature points can be extracted faster. Meanwhile, a method is proposed to filter out the features that do not meet the facial shape effectively through limiting the shape variables in the fitting process. The feature point model can prevent the illegal deformation in the fitting process, and make the extracted face feature point closer to the real position. The experimental results show that both time efficiency and accuracy of the improved facial feature point extraction algorithm are improved based on AAM.

参考文献/References:

[1] 黄萍. 基于正面图像的三维人脸快速重建技术研究与应用[D]. 厦门: 厦门大学, 2018.
[2] KASS M, WITKIN A, TERZOPOULOS D. Snakes: active contour models[J]. IJCV, 1988, 1(4): 321-331.
[3] COOTES T F, TAYLOR C J, COOPER D H, et al. Active shape models-their training and application[J]. Computer vision and image understanding, 1995, 61(1): 38-59.
[4] COOTES T F, EDWARDS G J, TAYLOR C J. Active appearance models[C]//Proceedings of the 5th European Conference on Computer Vision-Volume II-Volume II. London: Springer. 1998: 181-198.
[5] COSTEN N, COOTES T, EDWARDS G, et al. Simultaneous extraction of functional face subspaces[C]// Proceedings of 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Fort Collins, USA, 1999.
[6] 呼月宁, 张艳宁, 朱宇, 等. AAM在多姿态人脸特征点检测中的应用[J]. 计算机工程与应用, 2010, 46(12): 161-165
[7] 沈先耿, 吴薇. 一种改进的快速多姿态人脸特征点定位算法[J]. 中国科技论文, 2015, 10(8): 933-936
[8] 杨玮玥, 张倩, 王斌, 等. 3D建模的多姿态人脸合成方法[J]. 中国科技论文, 2018, 13(14): 1573-1577, 1602
[9] 龚卫国, 黄潇莹, 李伟红. 基于正弦变换的人脸姿态校正及识别研究[J]. 计算机工程与应用, 2010, 46(22): 213-216
[10] 郑明明, 林志毅. 基于局部统计几何特征的人脸性别分类方法[J]. 计算机工程与设计, 2019, 40(10): 2943-2948
[11] 吴火珍, 焦玉勇, 李海波, 等. 复杂区域三维有限元四面体网格自动生成研究[J]. 岩土力学, 2011, 32(11): 3479-3486
[12] 董瑞霞. 三维人脸数据的预处理及深度图像获取[J]. 福建电脑, 2017, 33(2): 18-19
[13] 陈路. 基于稀疏表达的人脸分析方法研究[D]. 成都: 电子科技大学, 2014.
[14] 赵明富, 陈兵, 宋涛, 等. 融合特征光流与角点特征的图像特征匹配算法研究[J]. 半导体光电, 2019, 40(4): 539-545, 549
[15] 王见, 陈义, 邓帅. 基于改进SVM分类器的动作识别方法[J]. 重庆大学学报, 2016, 39(1): 12-17

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备注/Memo

备注/Memo:
收稿日期:2020-01-08。
作者简介:闫保中，男，研究员;何伟，男，硕士研究生
通讯作者:何伟，E-mail：512658301@qq.com
更新日期/Last Update: 2021-02-05