Facial semantic representation for ethnical Chinese minorities based on geometric similarity
Facial semantic representation for ethnical Chinese minorities based on geometric similarity
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DOI：
10.1007/s13042-017-0726-0
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发表时间：
2017-09
影响因子：
5.6
通讯作者：
Cun-rui Wang;Qingling Zhang;X. Duan;Wanquan Liu;Jianhou Gan
中科院分区：
文献类型：
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作者：
Cun-rui Wang;Qingling Zhang;X. Duan;Wanquan Liu;Jianhou Gan
汉族人脸语义特征分析是人脸识别和人类学的重要研究课题之一。本文建立了一个包含三个民族的中国少数民族人脸数据库，并应用流形学习技术对人脸的民族特征进行分析，以实现判别语义表示。首先，我们以人类学家提出的脸部几何指标为基础，进行多方面的分析，最终发现这些指标在语义概念上是不可区分的。因此，有必要通过计算与地标相关联的完整距离、角度和指数来扩大面部特征的范围。然后，应用基于mRMR的特征选择方法，选取了2926个距离指标、21万多个角度指标和410万多个指标作为民族特征表示，得到了具有距离、角度、指标、人类学和组合特征的5个数据集。其次，利用LPP、ISOMAP、LE、PCA和LDA等几种流行的流形学习方法来研究上述获得的种族特征，结果显示5个数据集中的4个数据集的面部种族特征和聚类具有可区分的流形结构。为了评价过滤后特征的有效性，基于过滤后的特征，分别使用J48、SVM、RBF网络、贝叶斯网络和Weka中的贝叶斯网络进行分类。实验结果表明，组合特征数据集的平均分类准确率高于其他几何特征数据集，且相应指标比其他几何特征更显著。最后，基于民族人脸数据，发现了具有语义概念的子流形结构。中国三个民族的面部特征存在于低维空间中不同的民族语义子流形中。通过流形分析和特征选择得到的人脸测量指标不仅为人脸种族群体的计算分析提供了一种方法，而且丰富和完善了人类学的相关研究。
Facial semantic feature analysis for ethnical Chinese groups is one of the most significant research topics in face recognition and anthropology. In this paper, we build an ethnical Chinese face database including three ethnical groups, and then manifold learning technique is applied to analyze facial ethnic features for discriminant semantic representation. Firstly, we conduct manifold analysis on the basis of facial geometric indicators that are proposed by anthropologist, which are eventually shown not distinguishable in semantics concepts. Therefore, it is necessary to expand the scope of facial features by calculating the complete distances, angles and indexes associated with landmarks. Then, mRMR-based feature selection is applied to select 2926 distance indicators, more than 210,000 angle indicators and more than 4,100,000 index indicators ethnical feature representation, and 5 datasets with features of distance, angle, index, anthropology and combinations are obtained. Secondly, several popular manifold learning methods, such as LPP, ISOMAP, LE, PCA and LDA are utilized to investigate the ethnic features obtained above, and the results show the distinguishable manifold structure of facial ethnical features and clusters in 4 of the 5 datasets. In order to evaluate the validity of filtered features, the classification algorithms, J48, SVM, RBF Network, Bayesian, and Bayes Network in Weka, are carried out based on the filtered features. The experimental results reveal that the average of classification accuracy on the dataset with combined features is higher than other datasets, and the corresponding indexes are more salient than other geometric features. Finally, the sub-manifold structures with semantic concepts are found based on the ethnic facial data. Facial features of three Chinese ethnic groups exist in different ethnic semantic sub-manifolds in the low-dimensional space. Facial measurement indicators obtained by manifold analysis and feature selection provide not only a method for computational facial ethnic groups analysis, but also an enrichment and improvement to the related research in anthropology.