IEEE Trans Image Process. 2014 Aug;23(8):3294-307. doi: 10.1109/TIP.2014.2329451. Epub 2014 Jun 6.
For the task of robust face recognition, we particularly focus on the scenario in which training and test image data are corrupted due to occlusion or disguise. Prior standard face recognition methods like Eigenfaces or state-of-the-art approaches such as sparse representation-based classification did not consider possible contamination of data during training, and thus their recognition performance on corrupted test data would be degraded. In this paper, we propose a novel face recognition algorithm based on low-rank matrix decomposition to address the aforementioned problem. Besides the capability of decomposing raw training data into a set of representative bases for better modeling the face images, we introduce a constraint of structural incoherence into the proposed algorithm, which enforces the bases learned for different classes to be as independent as possible. As a result, additional discriminating ability is added to the derived base matrices for improved recognition performance. Experimental results on different face databases with a variety of variations verify the effectiveness and robustness of our proposed method.
对于鲁棒人脸识别任务，我们特别关注训练和测试图像数据由于遮挡或伪装而损坏的情况。以前的标准人脸识别方法，如特征脸或最新的基于稀疏表示分类的方法，都没有考虑到训练过程中数据可能受到污染的情况，因此它们在损坏的测试数据上的识别性能会下降。在本文中，我们提出了一种基于低秩矩阵分解的新的人脸识别算法来解决上述问题。除了能够将原始训练数据分解为一组代表性基以更好地建模人脸图像之外，我们还在提出的算法中引入了结构不和谐的约束，强制不同类别的学习基尽可能独立。因此，为派生的基矩阵添加了额外的鉴别能力，以提高识别性能。在具有各种变化的不同人脸数据库上的实验结果验证了我们提出的方法的有效性和鲁棒性。
