3D face recognition using facial curves, sparse random projection and fuzzy similarity measure

Naouar Belghini, Soufiane Ezghari, Azeddine Zahi
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Abstract

In this paper, we propose a fuzzy similarity based classification approach for 3D face recognition. In the feature extraction method, we exploit curve concept to represent the 3D facial data, two types of curves was considered: depth-level and depth-radial curves. As the dimension of the obtained features is high, the problem “curse of dimensionality” appears. To solve this problem, the Random Projection (RP) method was used. The proposed classifier performs Fuzzification operation using triangular membership functions for input data and ordered weighted averaging operators to measure similarity. Experiment was conducted using vrml files from 3D Database considering only one training sample per person. The obtained results are very promising for depth-level and depth-radial curves, besides the recognition rates are higher than 98%.
基于人脸曲线、稀疏随机投影和模糊相似度测度的三维人脸识别
本文提出了一种基于模糊相似度的三维人脸识别分类方法。在特征提取方法中,我们利用曲线的概念来表示三维人脸数据,考虑了两种类型的曲线:深度级曲线和深度-径向曲线。由于得到的特征维数很高,就出现了“维数诅咒”问题。为了解决这一问题,采用了随机投影(RP)方法。该分类器使用三角隶属函数对输入数据进行模糊化操作,并使用有序加权平均算子来测量相似度。实验使用3D数据库中的vrml文件进行,每人只考虑一个训练样本。所得结果对深度级曲线和深度-径向曲线具有良好的应用前景,识别率均在98%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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