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。
For the best feature, we also use the classifier based on KCCA to reclassify it.
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。
Kernel Canonical Correlation Analysis (KCCA) is a recently addressed supervised machine learning methods, which is a powerful approach of extracting nonlinear features.
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.
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