基于非高斯二维Gabor滤波器的生物特征提取算法

Gabor滤波器是一种非常有效的图像纹理特征提取算子。Gabor滤波器可以看作是高斯核函数在频域由复正弦函数调制而成，其频谱仍是高斯函数。采用Gabor滤波器对图像进行滤波处理时，图像所包含的位于高斯函数的频带范围之外的非高斯频谱上的重要信息并不能被Gabor滤波器所提取。提出另外一种二维非高斯Gabor滤波器用于生物特征提取。在所提二维非高斯Gabor滤波器中引入了一个新的参数。这个新参数可以控制二维非高斯Gabor滤波器包络的形状。为了证明所提出的非高斯Gabor滤波器的优越性，在人脸和掌纹数据库中做了大量的实验。实验结果表明，提出的二维非高斯Gabor滤波器的性能相比于传统二维Gabor滤波器有较大的提高。

Non-Gaussian Gabor filters for biometric feature extraction

Gabor filter has gained much reputation as a powerful and attractive texture operator in terms of accuracy. However, Gabor filter can be viewed as a Gaussian kernel modulated by a complex sinusoid in the frequency domain. Important information of images contained in the frequency bands lying outside the coverage of the Gaussian kernel bandwidth can not be extracted by Gabor filters. This paper develops another non-Gaussian Gabor filter with a new introduced parameter for texture feature extraction. The newly introduced parameter can control the shape of envelope of non-Gaussian Gabor filters. To demonstrate the superiority of the proposed non-Gaussian Gabor filter, extensive experiments on biometrics have been done. Experimental results have shown that the performance of the proposed non-Gaussian Gabor filter has larger superiority than Gabor filter.