A novel high-performance holistic descriptor for face retrieval
Özet
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. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
