论文标题
部分可观测时空混沌系统的无模型预测
Local Directional Gradient Pattern: A Local Descriptor for Face Recognition
论文作者
论文摘要
在本文中，提出了一个高阶段的局部模式描述符，以供面部识别。提出的局部定向梯度模式（LDGP）是通过在四个不同方向上编码参考像素的高阶衍生物之间的关系计算的一维局部微图案。所提出的描述符标识了四个不同方向上引用像素的高阶衍生物之间的关系，以计算与局部特征相对应的微图案。建议的描述符大大降低了微图案的长度，从而减少了提取时间和匹配时间，同时保持了识别率。在基准数据库AT＆T上进行的广泛实验的结果，扩展的Yale B和CMU-PIE表明，所提出的描述符显着减少了提取和匹配时间，而识别率几乎与现有的ART方法状态相似。
In this paper a local pattern descriptor in high order derivative space is proposed for face recognition. The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between the higher order derivatives of the reference pixel in four distinct directions. The proposed descriptor identifies the relationship between the high order derivatives of the referenced pixel in four different directions to compute the micropattern which corresponds to the local feature. Proposed descriptor considerably reduces the length of the micropattern which consequently reduces the extraction time and matching time while maintaining the recognition rate. Results of the extensive experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate is almost similar to the existing state of the art methods.
