Robust and Low-Rank Representation for Fast Face Identification With Occlusions
Robust and Low-Rank Representation for Fast Face Identification With Occlusions
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DOI：
10.1109/tip.2017.2675206
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发表时间：
2017-05-01
影响因子：
10.6
通讯作者：
Katsaggelos, Aggelos K.
中科院分区：
文献类型：
--
作者：
Iliadis, Michael;Wang, Haohong;Katsaggelos, Aggelos K.
本文提出了一种迭代的方法来解决块遮挡下的人脸识别问题。我们的方法利用基于两个特征的稳健表示，以便有效地对连续错误(例如，块遮挡)进行建模。第一种适合误差的分布由定制的损失函数来描述。第二种将误差图像描述为具有特定结构(导致与图像大小相比排名较低)。我们将证明这种联合表征对于描述具有空间连续性的误差是有效的。由于使用了乘子的交替方向法，我们的方法在计算上是有效的。我们的快速迭代算法的一种特殊情况导致了稳健表示方法，该方法通常用于处理非连续错误(例如，像素损坏)。在具有代表性的人脸数据库上(在受限和非受限环境中)的广泛结果证明了我们的方法在识别率和计算时间方面相对于现有的稳健表示方法的有效性。
In this paper, we propose an iterative method to address the face identification problem with block occlusions. Our approach utilizes a robust representation based on two characteristics in order to model contiguous errors (e.g., block occlusion) effectively. The first fits to the errors a distribution described by a tailored loss function. The second describes the error image as having a specific structure (resulting in low-rank in comparison with image size). We will show that this joint characterization is effective for describing errors with spatial continuity. Our approach is computationally efficient due to the utilization of the alternating direction method of multipliers. A special case of our fast iterative algorithm leads to the robust representation method, which is normally used to handle non-contiguous errors (e.g., pixel corruption). Extensive results on representative face databases (in constrained and unconstrained environments) document the effectiveness of our method over existing robust representation methods with respect to both identification rates and computational time.