Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization
Multi-scale Patch Based Collaborative Representation for Face Recognition with Margin Distribution Optimization
复制标题
DOI：
10.1007/978-3-642-33718-5_59
复制
发表时间：
2012-10
期刊：
影响因子：
--
通讯作者：
Peng Fei Zhu;Lei Zhang;Q. Hu;S. Shiu
中科院分区：
文献类型：
--
作者：
Peng Fei Zhu;Lei Zhang;Q. Hu;S. Shiu
在实际应用中，由于样本采集的困难，小样本是人脸识别中最具挑战性的问题之一。通过将查询样本表示为来自所有类别的训练样本的线性组合，所谓的基于协作表示的分类（CRC）以低计算成本显示出非常有效的人脸识别性能。然而，当每个受试者的可用训练样本非常有限时，CRC的识别率将急剧下降。这个问题的一个直观的解决方案是在补丁上运行CRC并组合所有补丁的识别输出。尽管如此，补丁大小的设置是一个不平凡的任务。考虑到不同尺度上的图像块具有互补的分类信息，提出了一种基于多尺度图像块的CRC方法，并通过正则化的边缘分布优化实现多尺度输出的集成。我们的大量实验验证了所提出的方法优于许多国家的最先进的基于补丁的人脸识别算法。
Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a non-trivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multi-scale patch based CRC method, while the ensemble of multi-scale outputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms.