Gender and Ethnic Classification of Face Images
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Performance of Geometrix ActiveID^TM 3D Face Recognition Engine on the FRGC Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Global texture analysis of iris images for ethnic classification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Multimodal facial gender and ethnicity identification
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Distinguishing Facial Features for Ethnicity-Based 3D Face Recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
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In this paper, we propose a novel fuzzy 3D face ethnicity categorization algorithm, which contains two stages, learning and mapping. In learning stage, the visual codes are first learned for both the eastern and western individuals using the learned visual codebook (LVC) method, then from these codes we can learn two distance measures, merging distance and mapping distance. Using the merging distance, we can learn the eastern, western and human codes based on the visual codes. In mapping stage, we compute the probabilities for each 3D face mapped to eastern and western individuals using the mapping distance. And the membership degree is determined by our defined membership function. The main contribution of this paper is that we view ethnicity categorization as a fuzzy problem and give an effective solution to assign the 3D face a reasonable membership degree. All experiments are based on the challenging FRGC2.0 3D Face Database. Experimental results illustrate the efficiency and accuracy of our fuzzy 3D face ethnicity categorization method.