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A novel high-performance holistic descriptor for face retrieval
Pattern Analysis and Applications ( IF 3.9 ) Pub Date : 2019-02-20 , DOI: 10.1007/s10044-019-00803-5
Nazife Çevik , Taner Çevik

Texture extraction-based classification has become the facto methodology applied in face recognition. Haralick feature extraction from gray-level co-occurrence matrix (GLCM) is one of the basic holistic studies that has inspired many face recognition algorithms. This paper presents a theoretically simple, yet efficient, holistic approach that utilizes the spatial relationships of the same pixel patterns occurring at different positions in an image rather than their occurrence statistics as applied in GLCM-based counterparts. The matrix holding the statistical values for the total displacement of the pixel patterns is called the gray-level total displacement matrix (GLTDM). Three approaches are proposed for feature extraction. In the first approach, classical Haralick features extraction is conducted. The second approach (D_GLTDM) utilizes the GLTDM directly as the feature vector rather than extra feature extraction process. In the last approach, principle component analysis (PCA) is used as the feature extraction method. Comprehensive simulations are conducted on images retrieved from the popular face databases, namely face94, ORL, JAFFE and Yale. The performance of the proposed method is compared with that of GLCM, local binary pattern and PCA used in the leading studies. The simulation results and their comparative analysis show that D_GLTDM exhibits promising results and outperforms the other leading methods in terms of classification accuracy.

中文翻译:

一种新颖的高性能整体描述符用于人脸检索

基于纹理提取的分类已成为应用于面部识别的事实方法。从灰度共现矩阵(GLCM)中提取Haralick特征是启发许多人脸识别算法的基础整体研究之一。本文提出了一种理论上简单但有效的整体方法,该方法利用了在图像中不同位置处发生的相同像素模式的空间关系,而不是像在基于GLCM的对应对象中所应用的发生率统计那样。保持像素图案的总位移的统计值的矩阵称为灰度总位移矩阵(GLTDM)。提出了三种特征提取方法。在第一种方法中,进行经典的Haralick特征提取。第二种方法(D_GLTDM)直接将GLTDM用作特征向量,而不是额外的特征提取过程。在最后一种方法中,主成分分析(PCA)被用作特征提取方法。对从流行的面部数据库(即face94,ORL,JAFFE和Yale)检索到的图像进行了全面的模拟。将该方法的性能与主要研究中使用的GLCM,局部二进制模式和PCA的性能进行了比较。仿真结果及其比较分析表明,D_GLTDM在分类精度上显示出令人鼓舞的结果,并且优于其他领先方法。对从流行的面部数据库(即face94,ORL,JAFFE和Yale)检索到的图像进行了全面的模拟。将该方法的性能与主要研究中使用的GLCM,局部二进制模式和PCA的性能进行了比较。仿真结果及其比较分析表明,D_GLTDM在分类精度上显示出令人鼓舞的结果,并且优于其他领先方法。对从流行的面部数据库(即face94,ORL,JAFFE和Yale)检索到的图像进行了全面的模拟。将该方法的性能与主要研究中使用的GLCM,局部二进制模式和PCA的性能进行了比较。仿真结果及其比较分析表明,D_GLTDM在分类精度上显示出令人鼓舞的结果,并且优于其他领先方法。
更新日期:2019-02-20
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