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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 6, 2023.
Abstract: Face detection and localization has been a major field of study in facial analysis and computer vision. Several convolutional neural network-based architectures have been proposed in the literature such as cascaded approach, single-stage and two-stage architectures. Using image segmentation based technique for object/face detection and recognition have been an alternative approach recently being employed. In this paper, we propose detection of faces by using U-net segmentation architectures. Motivated from DenseNet, a variant of U-net, called Semi-Dense U-Net, is designed in order to improve the binary masks generated by the segmentation model and further post-processed to detect faces. The proposed U-Net model have been trained and tested on FDDB, Wider face and Open Image dataset and compared with state-of-the-art algorithms. We could successfully achieve dice coefficient of 95.68% and average precision of 91.60% on a set of test data from OpenImage dataset.
Ganesh Pai and Sharmila Kumari M, “Semi-Dense U-Net: A Novel U-Net Architecture for Face Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140643
@article{Pai2023,
title = {Semi-Dense U-Net: A Novel U-Net Architecture for Face Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140643},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140643},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Ganesh Pai and Sharmila Kumari M}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.