目的 单演信号分析在人脸识别中得到了日益广泛的应用，然而其中的单演方向作为一种极为重要的几何信息却未能得到充分的利用。为此，提出了一种新的增强型单演方向差分算子对单演方向进行特征提取，进而提出了融合MBP（单演二值模式）和EPMOD（增强型单演方向差分模式）的人脸识别方法。方法 首先对图像进行多种尺度的单演滤波并分别提取图片的MBP特征和EPMOD特征，然后使用BFLD（基于分块的Fisher线性判别）分别对两种特征进行降维并增强两种特征的分类能力。最后，在得分级别上对两种特征进行融合并进行分类识别。结果 在ORL和CAS-PEAL人脸库上的实验表明，本文提出的EPMOD算法具有更小的时间复杂度和空间复杂度的前提下具有与MBP、LGBP相当甚至更好的识别效果。结论 本文提出了一种有效的人脸特征提取方法，实验表明本文提出的将EPMOD和MBP特征进行融合的方法能够显著地提高算法的最终识别率。

Objective Monogenic signal analysis has been increasingly used in face recognition. However, the monogenic orientation has not been fully utilized which as an extremely important geometric information. In this paper, a novel coding method named EPMOD (enhanced patterns of monogenic orientation difference) is proposed to extract the local orientation features. Then a new face recognition method fusing MBP (monogenic binary pattern) and EPMOD is proposed. Method First, MBP feature and EPMOD feature are extracted by using multi-scale monogenic filter; then, BFLD (block-based Fisher linear discrimination) is used to reduce the dimensionality of the two descriptors. Finally, the two kind of feature is fused at score level. Result The experimental results on the ORL and CAS-PEAL face databases validate that the proposed algorithm has better performance than or comparable performance than LGBP and MBP but with lower time and space complexity. Conclusion An effective facial feature extraction method is proposed in this paper, and the experimental results also show that our fusion approach can improve the recognition rate significantly.