针对维吾尔族人脸在光照以及部分遮挡下的辨识率下降和鲁棒性差的问题,提出了二维离散余弦变换(2DDCT)与方向边缘幅值模式(POEM)相融合的维吾尔族人脸识别算法.首先,把维吾尔族人脸图像分块处理,并使用2DDCT把其分块后的维吾尔族人脸图像转换为频域状态;其次,压缩维吾尔族人脸图像以排除维吾尔族人脸图像中无用信息,即中频部分与非低频部分,并进行二维离散余弦逆变换(IDCT)得到重构的维吾尔族人脸图像;然后,经POEM计算维吾尔族人脸图像的特征量得到其相应的POEM直方图并把直方图级联在一起,作为该中心特征点的POEM纹理直方图,得到维吾尔族人脸特征点的纹理特征信息;最后,采用深度学习算法进行分类识别.本文通过实验提出的算法,在自建的维吾尔族人脸库中能够进一步提高其人脸识别率,在维吾尔族人脸数据库中其运算速度也有很大提高.实验结果表明,该算法尤其是在维吾尔族人脸数据库中拥有较好的识别精度,具有很强的鲁棒性,特别是在光照以及部分遮挡下具有很强的优势.“,”Considering the inferior robustness of Uyghur face recognition under illumination and partial occlusion,this study proposes a Uyghur face recognition algorithm based on two-dimensional discrete cosine transform (2DDCT) and patterns of oriented edge magnitudes (POEM).The Uygur face images were partitioned into several blocks,and 2DDCT was used to transform the partitioned images into a frequency domain.The images were compacted and irrelevant information was excluded,i.e.,the medium-frequency portion and the low-frequency portion,and then a two-dimensional inverse discrete cosine transform (IDCT) was carried out to obtain a reconstructed Uygur face image.The POEM was then used to calculate the characteristic quantity of the Uygur face image to obtain the corresponding POEM histogram.All histograms were cascaded together as the POEM texture histogram of the central characteristic point to acquire the texture feature information of Uygur face feature point.Finally,a deep learning algorithm was used to classify recognition.The algorithm proposed in this paper can improve the face recognition rate and operation speed of a self-built Uyghur face database.Experimental results show that the algorithm has good recognition accuracy,especially for a Uyghur face database,and strong robustness,especially under illumination and partial occlusion.