论文标题
部分可观测时空混沌系统的无模型预测
Local Gradient Hexa Pattern: A Descriptor for Face Recognition and Retrieval
论文作者
论文摘要
从某种意义上说，这些描述符在不同的姿势，照明和照明条件下表现良好，在面部识别中使用的本地描述符具有稳健性。这些描述符的准确性取决于映射面部图像当地邻域中存在的关系的精度。在本文中，提出了局部梯度HEXA模式（LGHP），以识别参考像素及其相邻像素之间在不同导数方向的不同距离处之间的关系。歧视性信息存在于当地社区以及不同的导数方向。提出的描述符有效地将这些关系转化为二进制微图案，以最佳的精度区分阶级面部图像。将提出的描述符的识别和检索性能与最具挑战性和基准的面部图像数据库（即，耶鲁-B，CMU-PIE，颜色，颜色特殊和LFW）的最具挑战性和基准的面部图像数据库（即最具挑战性和基准的面部图像数据库）进行了比较。与最先进的描述符相比，拟议的描述符具有更好的识别和检索率。
Local descriptors used in face recognition are robust in a sense that these descriptors perform well in varying pose, illumination and lighting conditions. Accuracy of these descriptors depends on the precision of mapping the relationship that exists in the local neighborhood of a facial image into microstructures. In this paper a local gradient hexa pattern (LGHP) is proposed that identifies the relationship amongst the reference pixel and its neighboring pixels at different distances across different derivative directions. Discriminative information exists in the local neighborhood as well as in different derivative directions. Proposed descriptor effectively transforms these relationships into binary micropatterns discriminating interclass facial images with optimal precision. Recognition and retrieval performance of the proposed descriptor has been compared with state-of-the-art descriptors namely LDP and LVP over the most challenging and benchmark facial image databases, i.e. Cropped Extended Yale-B, CMU-PIE, color-FERET, and LFW. The proposed descriptor has better recognition as well as retrieval rates compared to state-of-the-art descriptors.
