基于空间约束低秩图的人脸识别

Low-rank Graph with Spatial Constraint for Face Recognition

低秩表示能够很好地揭示隐藏在数据中的全局结构信息并且对噪声具有很强的鲁棒性。基于图嵌入维数约简理论框架,提出了一种人脸识别算法,其利用低秩表示模型构建数据低秩图。此外,在低秩模型中引入数据空间约束项,构建一种具有空间约束的低秩图以提高识别效果。在ORL和PIE标准人脸数据库上进行实验,同传统的识别算法相比,结果显示所提出的算法在识别率和对噪声的鲁棒性上具有更好的表现。

The low-rank representation （LLR） model can reveal the subtle data structure information and show a strong robustness when dealing with noises. Based on the framework for graph embedding dimensionality reduction method, we proposed a face recognition algorithm which establishes low-rank graph using low-rank representation model. In addi- tion, we constructed a novel low-rank graph with spatial constraint by using spatial information of the tracked points to improve recognition performance. To demonstrate the effectiveness of the presented algorithm, our comparative experi- ments were conducted using ORL and PIE face image databases. Experimetal results show that the effectiveness and ro- bustness to noises are always better than other state-of-the-art methods.