A Novel Semantic Approach For Multi-Ethnic Face Recognition

This paper proposes a semantic concept method to recognize multi-ethnic people based on Axiomatic fuzzy set (AFS) theory with application to image analysis. There are two advantages of the proposed approach: (i) It can convert the facial features to semantic concepts and in such a way we bridge the semantic gap between low level pixel features and interpretable concepts. (ii) It can implement the logical operation of semantic concepts in the AFS framework. Technically, we first construct facial features utilizing the facial landmarks such as eyes, nose, mouth, and face contour. Second, we establish some corresponding semantic concepts to describe facial features. Finally, a set of the semantic concept rules are extracted to form a classifier aimed at identifying facial ethnic attributes. The efficacy of the proposed approach is verified on Chinese Multi-ethnic face database (CMFD), FEI and CK+. Meanwhile, we first demonstrate that the selected features have two obvious advantages: (1) these features can achieve better performance for ethical recognition than the features based on pixel values directly. (2) The selected features can be obtained via facial landmark detector regardless of the image resolutions. Then, we compare the proposed approach with some existing classifiers using the selected features, such as principal component analysis (PCA), C4.5, Decision table, Cart, Fuzzy Decision Tree (FDT) and Repeated Incremental Pruning to Produce Error Reduction (Ripper), extensive experiments show that our method exhibits a similar performance with these methods, which is demonstrated by Friedman test, however, our proposed approach can provide interpretability and comprehension capability.