IEEE Trans Pattern Anal Mach Intell. 2011 Aug;33(8):1561-76. doi: 10.1109/TPAMI.2010.220. Epub 2010 Dec 10.
In this paper, we present a sparse correntropy framework for computing robust sparse representations of face images for recognition. Compared with the state-of-the-art l(1)norm-based sparse representation classifier (SRC), which assumes that noise also has a sparse representation, our sparse algorithm is developed based on the maximum correntropy criterion, which is much more insensitive to outliers. In order to develop a more tractable and practical approach, we in particular impose nonnegativity constraint on the variables in the maximum correntropy criterion and develop a half-quadratic optimization technique to approximately maximize the objective function in an alternating way so that the complex optimization problem is reduced to learning a sparse representation through a weighted linear least squares problem with nonnegativity constraint at each iteration. Our extensive experiments demonstrate that the proposed method is more robust and efficient in dealing with the occlusion and corruption problems in face recognition as compared to the related state-of-the-art methods. In particular, it shows that the proposed method can improve both recognition accuracy and receiver operator characteristic (ROC) curves, while the computational cost is much lower than the SRC algorithms.
在本文中，我们提出了一种稀疏相关框架，用于计算人脸识别的鲁棒稀疏表示。与基于 l(1)范数的最先进的稀疏表示分类器（SRC）相比，我们的稀疏算法是基于最大相关准则开发的，该准则对离群值更不敏感。为了开发一种更具可操作性和实用性的方法，我们特别对最大相关准则中的变量施加非负约束，并开发了一种半二次优化技术，以交替的方式近似最大化目标函数，从而将复杂的优化问题简化为通过加权线性最小二乘问题学习稀疏表示，其中在每次迭代中都有非负约束。我们的广泛实验表明，与相关的最先进方法相比，所提出的方法在处理人脸识别中的遮挡和损坏问题时更具鲁棒性和高效性。特别是，它表明所提出的方法可以提高识别精度和接收者操作特性（ROC）曲线，而计算成本比 SRC 算法低得多。
