计算机科学 ›› 2014, Vol. 41 ›› Issue (4): 287-291.

• 图形图像与模式识别 • 上一篇    下一篇

基于深度图像的人脸模型特征点自动标定

李康,尚鹏,耿国华   

  1. 西北大学信息科学与技术学院 西安710127;西北大学信息科学与技术学院 西安710127;西北大学信息科学与技术学院 西安710127
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受973计划前期研究专项课题:文化遗产数字化生存中的表示与复原理论方法研究(2011CB311802),国家自然科学基金重点项目:颅面形态学和颅面重构的研究(60736008),国家自然科学基金面上项目:自动颅像重合身份认证关键技术研究(61172170)资助

Automatic Location of Feature Points on Three-dimensional Facial Model Based on Depth Image

LI Kang,SHANG Peng and GENG Guo-hua   

  • Online:2018-11-14 Published:2018-11-14

摘要: 准确标定人脸三维模型上的特征点是颅面形态学研究的关键问题之一。针对目前人脸特征点标定需要手工干预等问题,提出了一种基于深度图像的人脸三维模型特征点标定方法,该方法首先生成人脸三维模型的二维深度图像,然后采用SUSAN算子、灰度积分投影等方法在该图像上标定特征点,最终将标定好的特征点映射到人脸三维模型上,从而实现鼻尖点、嘴角点、眼角点以及耳朵处13个特征点的标定。实验结果表明,该方法可自动标定人脸模型特征点,准确获得特征点的位置,有效解决了人脸特征点标定因人工参与而带来的不准确性。

关键词: 人脸三维模型,特征点标定,二维深度图像,SUSAN算子

Abstract: Accurate location of feature points on the three-dimensional facial model is one of the key issues in the craniofacial morphological research.For manual intervention in the process of facial feature points,the paper proposed the method based on the depth image of 3D facial model to locate the feature points.First of all,it generates the two-dimensional depth image of three-dimensional facial model,and then uses SUSAN operator,gray-level integral projection method to locate the feature points on this image,and ultimately mas the located feature points to the facial model,thereby achieves location of thirteen points including nose tip point,mouth corner points,eye corner point and ears points.Experimental results show that the method can automatically locate feature points of the facial model,and accurately obtain the position of the feature points,and effectively solve the problem caused by human involvement of the facial feature point location.

Key words: Three-dimensional facial model,Feature points location,2D depth image,SUSAN operator

[1] Jeng Shi-hong,Liao Hong-yuan,Han Chin-chuan,et al.Facialfeature detection using geometrical face model:an efficient approach [J].Pattern Recognition,1998,1(3):273-282
[2] Cosar S,Cetin M.A graphical model based solution to the facial feature point tracking problem [J].Image and Vision Computing,2011,29(5):335-350
[3] Wong K W,Lam K M,Siu W C.An efficient algorithm for human face detection and facial feature extraction under different conditions [J].Pattern Recognition,2001,4(10):1993-2004
[4] Xue Zhong,Li S Z,Teoh E K.Bayesian shape model for facial feature extraction and recognition [J].Pattern Recognition,2003,6(12):2819-2833
[5] Shih F Y,Chuang C.Automatic extraction of head and faceboundaries and facial features [J].Information Sciences,2004,8:117-130
[6] Zheng Zhong-long,Jiong Jia,Chunjiang Duanmu,et al.Facialfeature localization based on an improved active shape mode[J].Information Sciences,2008,8(9):2215-2223
[7] Gizatdinova Y,Surakka V.Automatic edge-based localization of facial features from images with complex facial expressions[J].Pattern Recognition Letters,2010,31(15):2436-2446
[8] Zhou Yue,Li Yin,Wu Zheng,et al.Robust facial feature points extraction in color images [J].Engineering Applications of Artificial Intelligence,2011,4(1):195-200
[9] Wang Yin-jie,Chua C S,Ho Y K.Facial feature detection and face recognition from 2D and 3D images[J].Pattern Recognition Letters,2002,3(10):1191-1202
[10] Xu Cheng-hua,Tan Tie-niu,Wang Yun-hong,et al.Combininglocal features for robust nose location in 3D facial data[J].Pattern Recognition Letters,2006,7(13):1487-1494
[11] Feng Jun,Ip H H S,Lai L Y,et al.Robust point correspondence matching and similarity measuring for 3D models by relative angle-context distributions[J].Image and Vision Computing,2008,6(8):761-775
[12] 麻宏静.基于相对角聚类和支持向量机的人脸特征点定位技术研究[D].西安:西北大学,2010
[13] 王密宫,陈锻生,林超.基于局部形状图的三维人脸特征点自动定位[J].计算机应用,2010,30(5):1255-1258
[14] Smith S M,Brady J M.SUSAN—A New Approach to Low LevelImage Processing [J].International Journal of Computer Vision,1997,3(1):45-78
[15] 吴福培,张宪民,邝泳聪,等.无铅焊点鲁棒定位的灰度积分投影算法[J].华南理工大学学报,2009,7(9):98-102
[16] 刘晓宁.计算机辅助颅像重合技术的研究与实现[D].西安:西北大学,2003

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