Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (4): 177-179.DOI: 10.3778/j.issn.1002-8331.2009.04.050

• 图形、图像、模式识别 • Previous Articles     Next Articles

Improved KNN-SVM algorithm for gender recognition

ZHAGN Jian-ming,YANG Zhong,LI Wei   

  1. Department of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang,Jiangsu 212013,China
  • Received:2008-08-19 Revised:2008-10-23 Online:2009-02-01 Published:2009-02-01
  • Contact: ZHAGN Jian-ming

改进KNN-SVM的性别识别

张建明,杨 忠,李 巍   

  1. 江苏大学 计算机科学与通信工程学院,江苏 镇江 212013
  • 通讯作者: 张建明

Abstract: Improved KNN-SVM that combined Support Vector Machine(SVM) with weighted K Nearest Neighbor(KNN) is presented to improve the accuracy of gender recognition nearby SVM hyperplane.The algorithm gets optimal threshold by a few of known gender samples,then computes the distances from the test samples to the optimal superplane of SVM in feature space,recognizes gender after comparing the distance to threshold.The experiments show that the mixed algorithm can improve the accuracy compared to SVM and KNN-SVM without weight value.

Key words: facial gender recognition, Support Vector Machine(SVM), K-Nearest Neighbors(KNN) classification, optimal threshold

摘要: 针对支持向量机(SVM)在超平面附近进行性别识别的不准确性,引入进行加权的K近邻(KNN)算法。提出了结合加权KNN和SVM的改进KNN-SVM算法,该算法用少量已知性别样本自动确定加权KNN与SVM的最优分类阈值,并计算待识别样本和支持向量机所确定的超平面的距离,通过距离与阈值的比较进行性别识别。基于FERET人脸库进行性别实验,实验结果表明,该算法比SVM算法和不进行加权处理的KNN-SVM算法的识别率更高。

关键词: 人脸性别识别, 支持向量机, K近邻距离分类器, 最优阈值