关键词: 支持向量机, 级联, 分类器, 融合

Abstract: Traditional Support Vector Machine（SVM） when faced with a large sample training problem, the sample size is limited by hardware. Therefore, this paper proposes a face image gender classification algorithm based on hierarchy SVM and classifier fusion. Hierarchy SVM classifier can train the different samples in several levels by setting the threshold. Meanwhile, in the cascade of each layer, in order to reduce the influence of various factors which in the process of classifier recognition, it makes optimal classifier trained by different feature dimensions to fusion and through educe error in fusion to make neutral face samples have more clear classification. The experimental results under the same hardware conditions show that only 70, 000 samples can be contained one time to train by one-layer SVM, while more than 120, 000 samples are involved in four-layers SVM, the corresponding recognition rate is 96.7% vs. 99.1%.

Key words: Support Vector Machines（SVM）, hierarchy, classifier, fusion