@article {10.3844/jcssp.2016.464.470,
article_type = {journal},
title = {Structural Geodesic-Tchebychev Transform: An Image Similarity Measure for Face Recognition},
author = {Razzaq, Ali Nadhim and Hussain, Zahir M. and Mohammed, Hind Rustum},
volume = {12},
number = {9},
year = {2016},
month = {Nov},
pages = {464-470},
doi = {10.3844/jcssp.2016.464.470},
url = {https://thescipub.com/abstract/jcssp.2016.464.470},
abstract = {This work presents a new holistic measure for face recognition. Face recognition involves three steps: Face Detection, Feature Extraction and Matching. In the face detection process to identify the face area in face images, Viola-Jones algorithm has been used. Feature extraction is based on performing double-transformation, where discrete Tchebychev transform is performed on the geodesic distance transform of the grayscale image. Structural Similarity (SSIM) is applied to the resulting image double-transform to find matching factor with other image faces in the FEI (Brazilian) database. Performance is measured using a confidence criterion based on the similarity distance between the recognized person (best match) and the next possible ambiguity (second-best match). Simulation results showed that the proposed approach handles the face recognition efficiently as compared with SSIM.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}