Semantic facial description via axiomatic Fuzzy Set based clustering

In this paper, we developed a new method to extract semantic face descriptions by using an Axiomatic Fuzzy Set (AFS)-based clustering approach. First we used the landmark-based geometry features to represent facial components, and then developed a new feature selection algorithm to select some salient features based on dissimilarity defined in AFS. Finally, the AFS-based clustering technique was used to extract the high-level semantic concepts. Extensive experiments showed that the proposed method can achieve much better results than the conventional clustering approaches like K-means and Fuzzy c-means clustering (FCM).