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.
multimodal ethnicity classification three dimensional
Guangpeng Zhang Yunhong Wang
School of Computer Science and Engineering Beihang University Beijing 100191, China
国际会议
The Fifth International Conference on Image and Graphics(第五届国际图像图形学学术会议 ICIG 2009)
西安
英文
928-932
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）