2维Gabor滤波器已在文字、人脸和虹膜识别等方面得到广泛应用, Gabor滤波器在特征提取方面具有独特的优势，但高计算复杂度限制了应用。如何快速地利用Gabor滤波器进行识别成为当前研究的重点。提出了基于2维Gabor滤波器包络的人脸识别算法，通过忽略Gabor滤波器的正弦部分，保留高斯成分构造矩形包络，将椭圆滤波器转换为矩形滤波器进行特征提取，即可以在不影响特征提取性能的情况下，只计算滤波器的主要能量区间，忽略区间以外的部分，提高了运算速度。在Yale和ORL人脸库上的测试结果证明，该算法分类准确度优于Eigenface和Fisherface方法，且速度较传统的Gabor滤波器方法快20%,取得了满意的结果。

Gabor filter responses have successfully used in various important computer vision tasks, such as in texture segmentation, face detection, and iris pattern description. It is evident that Gabor filters have many advantageous or even superior properties for feature extraction. But if the computational complexity cannot be improved their application areas will remain limited. How quickly and accurately using Gabor filter was the identification of the characteristics to become the focus of current research. The paper present Gabor filter envelope based face recognition only the Gaussian part of the filter has to be taken into account; the envelope is similarly the smallest area which includes certain percent of the total filter energy, outside this area can be discarded with only negligible effect in accuracy. The effective envelope is an ellipse which can be encapsulated by a minimal size rectangle. The size of the rectangle may significantly reduce the computational complexity in the spatial domain filtering and save memory in the frequency domain filtering. Experiments using ORL and Yale database indicate that the improved method accuracy outperforms Eigenface and Fisherface algorithm. The new algorithm saves time 20% than traditional methods and gets satisfactory results.