GENDER CLASSIFICATION BASED ON FUZZY SVM

摘要：

Generalization ability is an important issue in gender classification. In this paper a gender classifier based on Fuzzy SVM (FSV1Y1) is developed to improve the generalization ability. The fuzzy membership used in FSVM indicates the relativity of one persons face with female/male faces set. This paper proposes a novel method of generating fuzzy membership function automatically based on Learning Vector Quantization (LVQ) learning process. The method doesnt rely on the apriori information of data and has strong robustness to variations such as illumination、 expression and so on. The gender classifier based on FSVM is evaluated on the FERET、 CAS-PEAL、 BUAA-IRIP face databases. The results show that the gender classifier presented in this paper can tolerate more variations and show good performance in generalization ability.

关键词： Gender classification Generalization ability Membership LVQ FSVM Adaboost Gabor wavelet.

作者: XUE-MING LENG YI-DING WANG

作者单位: Graduate University of Chinese Academy of Sciences, Beijing, 100080, China North China University of Technology, Beijing, 100041, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 1260-1264

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）