一种KAZE算法在人脸图像匹配中的应用

基于KAZE人脸图像匹配算法是通过加性算子分裂算法来进行非线性扩散滤波，从而解决高斯分解带来的边界模糊和细节丢失问题. 利用任意步长构造稳定的非线性尺度空间，寻找不同尺度归一化后的Hessian局部极大值点来实现特征点的检测，采用M-SURF来描述特征点，从而构造特征描述向量. 在VS2010和Opencv环境下分别对KAZE特征和SIFT特征实现人脸图像的匹配. 通过改变输入人脸图像的模糊度，旋转角度，尺度大小，亮度变化结合Matlab对KAZE，SIFT，SURF进行进一步的性能仿真实验. 实验结果表明，即使在高斯模糊，角度旋转，尺度变换和亮度变化等情况下依然保持良好的性能.

Application of KAZE Algorithm in Human Face Image Matching

The face images matching algorithm based on KAZE is to do nonlinear diffusion filtering by the additive operator splitting algorithm. In this way, the problem of blurred boundaries and detail missing can be solved. A stable nonlinear scale space is constructed by using arbitrary step to search the Hessian local maximum value point after different scales normalizing to detect feature points. By using M-SURE to describe the feature points, the feature vectors are constructed. The KAZE and SIFT feature are used to do face images matching under VS2010 and Opencv. By changing the blur level, angle of rotation, scale, change of brightness, a further simulation experiment can be conducted aiming at KAZE, SIFT, SURF in Matlab. The research result proves that the KAZE has better performance even if under the condition of Gaussian Blur, angle rotating, scale transformation and intensity roughness.