Research Article
New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP
@ARTICLE{10.4108/eai.20-10-2021.171547, author={Abdessalam Hattab and Ali Behloul}, title={New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={9}, number={34}, publisher={EAI}, journal_a={SIS}, year={2021}, month={10}, keywords={Face recognition, Adaptive Local Ternary Pattern (ALTP), Scale-Invariant Feature Transform (SIFT), Deep Learning, Transfer Learning, LinearSVC}, doi={10.4108/eai.20-10-2021.171547} }- Abdessalam Hattab
Ali Behloul
Year: 2021
New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP
SIS
EAI
DOI: 10.4108/eai.20-10-2021.171547
Abstract
Face recognition is one of the most important topics in biometrics, where it achieved great success under controlled scenarios. Still, its accuracy degraded significantly in unconstrained conditions. To meet this challenge, we proposed a handcraft method based on extracting important regions from the face image. We have been using Scale-Invariant Feature Transform (SIFT) besides the Adaptive Local Ternary Patterns (ALTP). We have achieved an accuracy of 99.75% on the ORL database and 94.70% on the FERET database. Then, we proposed a second method based on deep learning to achieve more accurate face recognition. The deep learning models failed to achieve a high accuracy rate because they require a large amount of training data. We used firstly Data Augmentation to solve this failure. However, this solution does not show high performance. Secondly, our proposed ImageNet pre-trained AlexNet-v2 and VGG16 models with LinearSVC increased the accuracy rate to 100% for the both databases.
Copyright © 2021 Abdessalam Hattab et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
