Kim Jongsun, Choi Jongmoo, Yi Juneho, Turk Matthew
School of Information & Communication Engineering, Biometrics Engineering Research Center, Sungkyunkwan University, Korea.
IEEE Trans Pattern Anal Mach Intell. 2005 Dec;27(12):1977-81. doi: 10.1109/TPAMI.2005.242.
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 II, LFA, and LNMF methods, especially in the cases of partial occlusions and local distortions.
使用子空间投影的人脸识别方法的性能直接与其基图像的特征相关，特别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，该方法生成的基图像应表现出基于部分的局部表示。我们提出了一种有效的基于部分的局部表示方法，称为局部显著独立成分分析（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分的局部显著信息，以最大化应用“基于部分的识别”思想的益处。它通过在计算独立成分分析架构I基图像的过程中施加额外的定位约束来创建基于部分的局部基图像。我们将LS-ICA方法与其他基于部分的表示方法进行了对比，如局部非负矩阵分解（LNMF）和局部特征分析（LFA）。实验结果表明，LS-ICA方法的性能优于主成分分析（PCA）、独立成分分析架构I、独立成分分析架构II、LFA和LNMF方法，特别是在部分遮挡和局部失真的情况下。
