计算机科学与探索 ›› 2017, Vol. 11 ›› Issue (8): 1305-1313.DOI: 10.3778/j.issn.1673-9418.1605050

• 人工智能与模式识别 • 上一篇    下一篇

曲线相似度眼型分类

孙劲光,荣文钊+   

  1. 辽宁工程技术大学 电子与信息工程学院,辽宁 葫芦岛 125105
  • 出版日期:2017-08-01 发布日期:2017-08-09

Eye Shape Classification of Curve Similarity

SUN Jinguang, RONG Wenzhao+   

  1. School of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Online:2017-08-01 Published:2017-08-09

摘要: 针对人脸面部图像中人眼型分类问题,利用眼睑曲线的不同形状,提出一种基于眼睑轮廓曲线相似度与眼型指数相结合的人眼分类方法。采用基于轮廓的形状描述方法,在获取眼睛初始特征点的基础上增加采样点;然后根据上、下眼睑采样点与内、外眼角点,通过最小二乘法拟合得到上、下眼睑曲线方程;通过眼睑曲线方程计算出上、下眼睑采样点的斜率,利用归一化互相关系数描述上、下眼睑采样点斜率相似度;通过眼睑曲线方程计算眼型指数,与斜率相似度结合使用,达到眼型分类的目的。主要贡献是:定义了上、下眼睑轮廓曲线及其相似度,通过关联眼型指数进行眼型分类。使用该方法对标准眼、圆眼、眯缝眼、细长眼4种眼型进行分类,获得了85.17%的正确率。实验表明,所提方法易于实现,并且具有良好的眼型分类效果。

关键词: 眼型分类, 眼睑曲线, 眼型指数, 曲线相似度, 最小二乘法, 曲线拟合

Abstract:  Aiming at the problem of human eye classification in facial images, this paper utilizes different types of eyelid and proposes an approach to classify ocular types based on the combination of eyelid contour curve similarity and ocular type index. The description method of contour types is introduced, the sampling points are added on account of obtaining the initial feature points of the eyes; Then on the basis of upper and lower eyelid curves and   inner and outer corner of the eyes, the equation of eyelid contour curve is obtained by the least square fitting method according to the sampling points of the upper and lower eyelids; The slope of upper and lower eyelid sampling points are calculated by the eyelid contour curve equation, the slope similarity of upper and lower eyelid sampling points is described by the normalized cross-correlation coefficient; Finally, ocular type index can be calculated by the eyelid contour curve equation, which combined with slope similarity, can achieve the purpose of ocular types classification. The main contributions of this paper are the definition of upper and lower eyelid contour curves and the similarity between them, and the related ocular type index can be used in ocular type classification. The proposed method reaches an accuracy of 85.17% on the four eye shapes of round, standard, slender, narrow. The experiments prove that the proposed algorithm is easy to implement and can obtain better classification results.

Key words: eye shape classification, eyelid curve, ocular type index, curve similarity, the least square approximation, curve fitting