Tsinghua Center for Mobile Computing, Institute of Microelectronics, Tsinghua University, Beijing 100084, China.
Sensors (Basel). 2013 Apr 3;13(4):4499-513. doi: 10.3390/s130404499.
How to describe an image accurately with the most useful information but at the same time the least useless information is a basic problem in the recognition field. In this paper, a novel and high precision feature called BG2D2LRP is proposed, accompanied with a corresponding face recognition system. The feature contains both static texture differences and dynamic contour trends. It is based on Gabor and LBP theory, operated by various kinds of transformations such as block, second derivative, direct orientation, layer and finally fusion in a particular way. Seven well-known face databases such as FRGC, AR, FERET and so on are used to evaluate the veracity and robustness of the proposed feature. A maximum improvement of 29.41% is achieved comparing with other methods. Besides, the ROC curve provides a satisfactory figure. Those experimental results strongly demonstrate the feasibility and superiority of the new feature and method.
如何用最有用的信息但同时又是最少无用的信息来准确地描述一个图像，这是识别领域中的一个基本问题。在本文中，提出了一种新颖的、高精度的特征，称为 BG2D2LRP，并配有相应的人脸识别系统。该特征包含了静态纹理差异和动态轮廓趋势。它基于 Gabor 和 LBP 理论，通过各种变换（如块、二阶导数、直接方向、层等）操作，并以特定的方式融合。本文使用了 FRGC、AR、FERET 等七个著名的人脸数据库来评估所提出特征的准确性和鲁棒性。与其他方法相比，该方法的最大改进达到了 29.41%。此外，ROC 曲线提供了一个令人满意的结果。这些实验结果充分证明了新特征和方法的可行性和优越性。
