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首页> 外文期刊>Journal of Computers >Accelerated Kernel CCA plus SVDD: A Threestage Process for Improving Face Recognition
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Accelerated Kernel CCA plus SVDD: A Threestage Process for Improving Face Recognition

机译：加速内核CCA加SVDD：改善人脸识别的三种动画过程

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—kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for face classification and other applications. However, the standard KCCA algorithm may suffer from computational problem as the training set increase. To overcome the drawback, we propose a threestage method to improve the performance of KCCA. Firstly, a scheme based on geometrical consideration is proposed to enhance the extraction efficiency. The algorithm can select a subset of samples whose projections in feature space (Hilbert space) are sufficient to represent all of the data in feature space. Subsequently, an improved algorithm inspired by principal component analysis (PCA) is developed. The algorithm can select the most contributive eigenvectors for training and classification instead of considering all the ones. Finally, a multi-class classification method based on support vectors data description (SVDD) is employed to further enhance the recognition performance as it can avoid the repeated use of training data. The theoretical analysis and the experiment results demonstrate the effectiveness of improvements.
机译：-Kernel Canonical相关性分析（KCCA）是最近解决的监督机器学习方法，该方法显示是提取面部分类和其他应用的非线性特征的强大方法。然而，标准的KCCA算法可能遭受计算问题，因为训练集增加。为了克服缺点，我们提出了一种改进KCCA性能的三种方法方法。首先，提出了一种基于几何考虑的方案来提高提取效率。该算法可以选择特征空间中的投影（Hilbert Space）的样本子集足以表示特征空间中的所有数据。随后，开发了由主成分分析（PCA）启发的改进算法。该算法可以选择用于培训和分类的最贡献的特征向量，而不是考虑所有的特征向量。最后，采用基于支持向量数据描述（SVDD）的多级分类方法来进一步增强识别性能，因为它可以避免重复使用训练数据。理论分析和实验结果表明了改进的有效性。

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