Face recognition from an image/video has been a fast-growing area in research community, and a sizeable number of face recognition techniques based on texture analysis have been developed in the past few years. Further, these techniques work well on gray-scale and colored images, but very few techniques deal with binary and low-resolution images. As the binary image is becoming the preferred format for low face resolution analysis, there is a need for further studies to provide a complete solution for the image-based face recognition system with a higher accuracy rate. To overcome the limitation of the existing methods in extracting distinctive features in low-resolution images due to the contrast between the face and background, we propose a statistical feature analysis technique to fill the gaps. To achieve this, the proposed technique integrates the binary-level occurrence matrix (BLCM) and the fuzzy local binary pattern (FLBP) named FBLCM to extract global and local features of the face from binary and low-resolution images. The purpose of FBLCM is to distinctively improve performance of edge sharpness between black and white pixels in the binary image and to extract significant data relating to the features of the face pattern. Experimental results on Yale and FEI datasets validate the superiority of the proposed technique over the other top-performing feature analysis methods. The developed technique has achieved the accuracy of 94.54% when a random forest classifier is used, hence outperforming other techniques such as the gray-level co-occurrence matrix (GLCM), bag of word (BOW), and fuzzy local binary pattern (FLBP), respectively.

中文翻译：

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基于统计特征分析的全局和局部人脸识别新方法

来自图像/视频的面部识别一直是研究领域中快速发展的领域，并且在过去几年中已经开发了许多基于纹理分析的面部识别技术。此外，这些技术在灰度和彩色图像上效果很好，但是处理二进制和低分辨率图像的技术很少。由于二进制图像正成为低面部分辨率分析的首选格式，因此需要进行进一步研究，以为具有较高准确率的基于图像的面部识别系统提供完整的解决方案。为了克服由于面部和背景之间的对比度而在低分辨率图像中提取特征的现有方法的局限性，我们提出了一种统计特征分析技术来填补空白。为了达成这个，所提出的技术将二进制级别的出现矩阵（BLCM）和称为FBLCM的模糊局部二进制模式（FLBP）集成在一起，以从二进制和低分辨率图像中提取面部的全局和局部特征。FBLCM的目的是显着提高二进制图像中黑白像素之间边缘清晰度的性能，并提取与面部图案特征有关的重要数据。在Yale和FEI数据集上的实验结果证明了该技术优于其他性能最高的特征分析方法的优越性。使用随机森林分类器时，开发的技术已达到94.54％的精度，因此优于其他技术，例如灰度共现矩阵（GLCM），词袋（BOW）和模糊局部二进制模式（FLBP） ）， 分别。