论文标题
高阶局部定向模式基于锥体多层结构，可用于稳健的面部识别
High Order Local Directional Pattern Based Pyramidal Multi-structure for Robust Face Recognition
论文作者
论文摘要
源自本地邻域中纹理的一般定义，局部定向模式（LDP）编码像素的小局部3x3邻域中的方向信息，该信息可能无法提取详细信息，尤其是在因照明变化引起的输入图像变化时。因此，在本文中，我们介绍了一种新型的特征提取技术，该技术计算了第n阶方向变化模式，称为高阶局部方向模式（HOLDP）。所提出的Holdp可以比常规的最不发达国家捕获更多详细的判别信息。与LDP操作员不同，我们提出的技术通过在金字塔多结构方式中编码从像素的每个邻域层中编码各种独特的空间关系来提取n阶局部信息。然后，我们将每个邻层层的特征向量串联以形成最终的Holdp特征向量。拟议的Holdp算法的性能评估是在几个公开可用的面部数据库上进行的，并观察到在极端照明条件下Holdp的优势。
Derived from a general definition of texture in a local neighborhood, local directional pattern (LDP) encodes the directional information in the small local 3x3 neighborhood of a pixel, which may fail to extract detailed information especially during changes in the input image due to illumination variations. Therefore, in this paper we introduce a novel feature extraction technique that calculates the nth order direction variation patterns, named high order local directional pattern (HOLDP). The proposed HOLDP can capture more detailed discriminative information than the conventional LDP. Unlike the LDP operator, our proposed technique extracts nth order local information by encoding various distinctive spatial relationships from each neighborhood layer of a pixel in the pyramidal multi-structure way. Then we concatenate the feature vector of each neighborhood layer to form the final HOLDP feature vector. The performance evaluation of the proposed HOLDP algorithm is conducted on several publicly available face databases and observed the superiority of HOLDP under extreme illumination conditions.
