Abstract: Feature extraction is the most important step that affects the recognition accuracy of face recognition. One of these features are the texture descriptors that are playing an important role as local features descriptor in many of the face recognition systems. Recently, many types of texture descriptors had been proposed and used for face recognition task. The Completed Local Ternary Pattern (CLTP) is one of the texture descriptors that has been proposed for texture image classification and had been tested for different image classification tasks. It proposed to overcome the Local Binary Pattern (LBP) drawbacks where the CLTP is more robust to noise as well as shown a good discriminative property than others. In this paper, a comprehensive study on the performance of the CLTP for face recognition task has been done. The aim of this study is to investigate and evaluate the CLTP performance using eight different face datasets and compared with the previous texture descriptors. In the experimental results, the CLTP had been shown good recognition rates and outperformed the other texture descriptors for this task. Several face datasets are used in this paper, such as Georgia Tech Face, Collection Facial Images, Caltech Pedestrian Faces, JAFFE, FEI, YALE, ORL, UMIST datasets.

Sam Yin Yee, Taha H. Rassem, Mohammed Falah Mohammed and Nasrin M. Makbol. “Performance Evaluation of Completed Local Ternary Pattern (CLTP) for Face Image Recognition”. International Journal of Advanced Computer Science and Applications (IJACSA) 10.4 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100446

@article{Yee2019,
title = {Performance Evaluation of Completed Local Ternary Pattern (CLTP) for Face Image Recognition},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100446},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100446},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {4},
author = {Sam Yin Yee and Taha H. Rassem and Mohammed Falah Mohammed and Nasrin M. Makbol}
}