A novel feature fusion algorithm based on KCCA is established.
提出一种新的KCCA特征融合算法。
For the best feature, we also use the classifier based on KCCA to reclassify it.
对检测率最高的特征，我们用基于核典型相关分析的分类器重新分类。
The experimental work shows that the KCCA based classier outperform SVM, especially when the embedding rate is low.
实验结果表明，其检测率超过支撑向量机，在嵌入量低时，效果尤为明显。
Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.
针对该问题，采用核典型相关分析方法进行原始特征的二次提取，得到简约而重要的二次特征。
By introducing the kernel trick to the canonical correlation analysis(CCA), a feature fusion method based on kernel CCA(KCCA) is established and is then used to capture the associated feat.
该方法首先采集侧面视角人脸图像，然后将核方法引入到典型相关分析（CCA）中，提出基于核CCA的特征融合方法，并应用其提取人耳人脸的关联特征进行个体的分类识别。
By introducing the kernel trick to the canonical correlation analysis(CCA), a feature fusion method based on kernel CCA(KCCA) is established and is then used to capture the associated feat.
该方法首先采集侧面视角人脸图像，然后将核方法引入到典型相关分析（CCA）中，提出基于核CCA的特征融合方法，并应用其提取人耳人脸的关联特征进行个体的分类识别。
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