Face Recognition based on Convolution Neural Network and Scale Invariant Feature Transform

Abstract: Recently, Face Recognition (FR) has been received wide attention from both the research community and the cyber security industrial companies. Low accuracy of recognition is considered a main challenge when it comes to talking about employing the Artificial Intelligence (AI) for FR. In this work, the Scale Invariant Feature Transform (SIFT) and the Convolutional Neural Networks (CNN) feature extraction methods are utilized to build an AI based classifier. The CNN extracts features through both the convolutional and polling layers, while the SIFT extracts features depending on the scale space, directions, and histograms of points of interest. The features that are extracted by the CNN and the SIFT methods are used as an inputs for the KNN classifier. The experimental results with 400 test images of 40 persons, with 240 images are randomly chosen as training sets and 160 images from test sets, demonstrate in terms of accuracy, sensitivity, and error rate, that the CNN-based KNN classifier achieved better results when compared to the SIFT-based KNN classifier (accuracy = 97%, sensitivity = 93%, error rate = 3%).

Jamilah ALAMRI, Rafika HARRABI and Slim BEN CHAABANE. “Face Recognition based on Convolution Neural Network and Scale Invariant Feature Transform”. International Journal of Advanced Computer Science and Applications (IJACSA) 12.2 (2021). http://dx.doi.org/10.14569/IJACSA.2021.0120281

@article{ALAMRI2021,
title = {Face Recognition based on Convolution Neural Network and Scale Invariant Feature Transform},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2021.0120281},
url = {http://dx.doi.org/10.14569/IJACSA.2021.0120281},
year = {2021},
publisher = {The Science and Information Organization},
volume = {12},
number = {2},
author = {Jamilah ALAMRI and Rafika HARRABI and Slim BEN CHAABANE}
}