改进的Gabor小波变换特征提取算法

Feature extraction algorithm based on improved Gabor wavelet transform

针对基于Gabor小波幅值与相位的人脸特征提取方法的特征级联方式使得特征向量维度较高的问题,提出了一种改进的Gabor小波变换特征提取算法。该算法计算局部幅值特征和局部相位特征,增强了每个像素的局部关联性;然后通过实验选定加权系数,将幅值特征与相位特征进行加权融合。实验结果表明,该算法与改进前的算法相比,降低了特征向量的维度,且提高了最终的人脸识别率。

Aiming at the problem that the feature cascading method for face feature extraction based on Gabor wavelet amplitude and phase makes the feature vector dimension higher,this paper proposed an improved Gabor wavelet transform feature extraction algorithm.The algorithm calculated local amplitude and local phase features,and enhanced the local correlation of each pixel.Then the algorithm weighted fusion for amplitude and phase features by weighting coefficients which experiment selected.Experimental results show that the proposed algorithm reduces the dimension of the feature vector and improves the final face recognition rate compared with the pre improved algorithm.