Face Image Gender Identification Based on Cascade Connection Support Vector Machine
Kunlun Li, Liao Pin · Jisuanji gongcheng · 2012
Support Vector Machine(SVM) is a popular statistical learning methods,but large-scale training of SVM is limited by hardware.This paper proposes a face image gender classification algorithm based on a cascade connection SVM,which filters the easily classified samples by pre-layer classifiers,and re-organizes the left tough samples to train the next SVM layer.Meanwhile,more samples are used,and the classifier has better recognition performance.Experimental results under the same hardware conditions show that only 70 000 samples can be contained one time to train one-layer SVM,while more than 120 000 samples are involved in four-layer SVM,the corresponding recognition rate is 96.6% to 98.4%.