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Effective representation using ICA for face recognition robust to local distortion and partial occlusion

机译:使用ICA进行人脸识别的有效表示,对局部失真和部分遮挡具有鲁棒性

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The performance of face recognition methods using subspace projection is directly related to the characteristics of their basis images, especially in the cases of local distortion or partial occlusion. In order for a subspace projection method to be robust to local distortion and partial occlusion, the basis images generated by the method should exhibit a part-based local representation. We propose an effective part-based local representation method named locally salient ICA (LS-ICA) method for face recognition that is robust to local distortion and partial occlusion. The LS-ICA method only employs locally salient information from important facial parts in order to maximize the benefit of applying the idea of "recognition by parts". It creates part-based local basis images by imposing additional localization constraint in the process of computing ICA architecture I basis images. We have contrasted the LS-ICA method with other part-based representations such as LNMF (localized nonnegative matrix factorization) and LFA (local feature analysis). Experimental results show that the LS-ICA method performs better than PCA, ICA architecture I, ICA architecture 11, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.
机译:使用子空间投影的面部识别方法的性能直接与其基本图像的特性相关,尤其是在局部失真或部分遮挡的情况下。为了使子空间投影方法对局部失真和部分遮挡具有鲁棒性,该方法生成的基础图像应显示基于零件的局部表示。我们提出了一种有效的基于局部的局部表示方法,称为局部显着ICA(LS-ICA)方法,用于人脸识别,该方法对局部失真和部分遮挡具有鲁棒性。 LS-ICA方法仅采用来自重要面部部位的局部显着信息,以最大程度地应用“部位识别”的思想。它通过在计算ICA体系结构I基础图像的过程中施加附加的本地化约束来创建基于零件的局部基础图像。我们已经将LS-ICA方法与其他基于零件的表示法(例如LNMF(局部非负矩阵分解)和LFA(局部特征分析))进行了对比。实验结果表明,LS-ICA方法的性能优于PCA,ICA体系结构I,ICA体系结构11,LFA和LNMF方法,尤其是在部分遮挡和局部失真的情况下。

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