Facial semantic representation for ethnical Chinese minorities based on geometric similarity

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.