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Construction and Application of SVM Model and Wavelet-PCA for Face Recognition

机译:支持向量机模型和小波PCA在人脸识别中的构建与应用

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This work presents a method to increase the face recognition accuracy using a combination of Wavelet, PCA, and SVM. Pre-processing, feature extraction and classification rules are three crucial issues for face recognition. This paper presents a hybrid approach to employ these issues. For preprocessing and feature extraction steps, we apply a combination of wavelet transform and PCA. During the classification stage, SVMs incorporated with a binary tree recognition strategy are applied to tackle the multi-class face recognition problem to achieve a robust decision in presence of wide facial variations. The binary trees extend naturally, the pairwise discrimination capability of the SVMs to the multiclass scenario. Two face databases are used to evaluate the proposed method. The computational load of the proposed method is greatly reduced as comparing with the original PCA based method on the ORL and Compound face databases. Moreover, the accuracy of the proposed method is improved.
机译:这项工作提出了一种结合使用Wavelet,PCA和SVM来提高面部识别精度的方法。预处理,特征提取和分类规则是面部识别的三个关键问题。本文提出了一种混合方法来解决这些问题。对于预处理和特征提取步骤,我们将小波变换和PCA结合使用。在分类阶段,结合了二叉树识别策略的SVM被用于解决多类人脸识别问题,从而在存在宽广的面部变化的情况下做出可靠的决策。二进制树自然地扩展,SVM的成对区分能力扩展到多类方案。使用两个人脸数据库来评估所提出的方法。与原始的基于PCA的ORL和Compound人脸数据库相比,该方法的计算量大大减少。此外,所提出的方法的准确性得以提高。

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