Multimodal 2D and 3D facial ethnicity classification

Ethnicity is an important demographic attribute of human beings, and automatic face-based classification of ethnicity has promising applications in various fields. In this paper, we explore the ethnicity discriminability of both 2D and 3D face features, and propose an MM-LBP (Multi-scale Multi-ratio LBP) method, which is a multimodal method for ethnicity classification. LBP (Local Binary Pattern) histograms are extracted from multi-scale, multi-ratio rectangular regions over both texture and range images, and Adaboost is utilized to construct a strong classifier from a large amount of weak classifiers built by the extracted LBP histograms. Decision level fusion is performed to get the final decision. Experiments performed on FRGC v2.0 database indicate that the fusion of 2D and 3D face features significantly improves the classification accuracy, and the proposed MM-LBP method has consistent higher performance for ethnicity classification than traditional methods. Above 99.5% classification accuracy was obtained on the FRGC v2.0 database.