基于Gabor和支持向量机的人脸识别

支持向量机(SVM)在处理小样本高维数据及泛化性能强等方面的优势,以及Gabor小波可以很好地模拟哺乳动物视觉神经简单细胞的感受野轮廓降低外界因素的影响,提出了基于Gabor与SVM的人脸识别方法。通过对经Gabor变换人脸图像的独立成分分析得到一组Gabor人脸独立基,并且用遗传算法求得一组最优的Gabor独立基,不但可以降低特征维数,减少计算量,而且可以提高识别率。通过对耶鲁大学人脸图像数据库的测试,证实本文算法有效性。

A Face Recognition Method Based on Gabor and Support Vector Machine

Based on the high performance of support vector machine (SVM) in tackling small sample size, high dimension and its good generalization,and Gabor's biological relevance and computational properties,this paper proposed a face recognition method based on Gabor and SVM. To decrease the computing complexity and improve the recognition rate,Genetic Algorithms (GA) is used to select the optimal feature set for classification. The experiments in the Yale face database show that this algorithm is very effective.