【摘要】 二维最大散度差线性鉴别分析方法不仅有效地避免了在人脸识别中传统的Fisher线性鉴别分析通常存在的"小样本问题",而且使其特征抽取的速度有了大幅度的提高.本文通过引入著名的"核技巧",将二维最大散度差线性鉴别分析扩展到非线性空间,提出了一种新的二维核最大散度差鉴别分析方法.该方法不仅抽取了图像中更加有效的非线性鉴别特征,使正确识别率显著提高,而且为二维非线性鉴别分析提供了一个统一的构架.最后在AR标准人脸库中的实验结果验证了本文算法的有效性.更多还原

【Abstract】 Two-dimensional maximum scatter-difference discriminant analysis not only essentially avoids the small sample size problem occurred in traditional Fisher discriminant analysis,but also saves much computational time for feature extraction.In this paper,a novel two-dimensional kernel maximum scatter difference discriminant analysis(2D-KMSDA) is developed for the extraction of nolinear feature by using the well-known "kernel trick".It extracts much more effective nolinear feature and made the true recognition rate raise saliently.What’s more,it also offers a unified framework for two-dimensional nolinear discriminant analysis.Finally,extensive experiments performed on AR face database verify the effectiveness of the proposed method.更多还原