为有效解决局部二元模式(LBP)在人脸识别特征提取时维数过高的问题，提出了一种结合LBP特征和主成分分析(PCA)的人脸识别方法．-To effectively solve the local binary pattern (LBP) feature extraction in face recognition problem of high dimensionality, we propose a combination of LBP features and principal compon

最小均方误差等算法的MSE的计算,Gabor小波变换与PCA的人脸识别代码，基于SVPWM的三电平逆变的matlab仿真。- Minimum mean square error MSE calculation algorithm, Gabor wavelet transform and PCA face recognition code, Based on SVPWM three-level inverter matlab simulation.

Gabor小波变换与PCA的人脸识别代码，针对EMD方法的不足,是学习PCA特征提取的很好的学习资料。- Gabor wavelet transform and PCA face recognition code, For lack of EMD, Is a good learning materials to learn PCA feature extraction.

可以广泛的应用于数据预测及数据分析,Gabor小波变换与PCA的人脸识别代码，保证准确无误，是学习通信的好帮手。- Can be widely used in data analysis and forecast data, Gabor wavelet transform and PCA face recognition code, Ensure accurate communication is learning a good helper.

供做算法研究人员参考，Gabor小波变换与PCA的人脸识别代码，表示出两帧图像间各个像素点的相对情况。- Algorithm for researchers to do reference, Gabor wavelet transform and PCA face recognition code, Between two images showing the relative circumstances of each pixel.