基于相对梯度的人脸识别方法

在原始相对梯度算子的基础上，提出一种新的相对梯度算子,并将它与二维主成分分析（2DPCA）或者二维Fisher线性判别分析（2DFLD）相结合，形成一种基于改进相对梯度算子的人脸识别算法。在AR库和Yale_B库上的实验表明，基于改进相对梯度算子的人脸识别算法对人脸图像的光照、表情等变化均具有较好的鲁棒性，识别准确率明显高于只用2DPCA或2DFLD进行特征抽取的人脸识别方法，以及基于原始相对梯度算子的人脸识别算法。同时采用三种不同大小的窗口分别进行实验，实验结果证明，当窗口大小为3×3时，识别效果相对最好。

Face recognition method based on relative gradient

Based on the original relative gradient operator, the authors proposed a new relative gradient operator and combined it with 2DPCA or 2DFLD. A face recognition algorithm based on this new relative gradient operator was also put forward. Experimental results on AR and Yale_B face database show that, the method has robustness to the complex change like various expression and lighting condition. The recognition accuracy of the method is much higher than 2DPCA, 2DFLD and face recognition based on the original relative gradient operator. What is more, experiments were done on three different sizes of windows, which confirms that, when the window size is 3×3, the recognition result is relatively the best.