sis 18: e11

Research Article

New Approaches for Automatic Face Recognition Based on Deep Learning Models and Local Handcrafted ALTP

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  • @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: Online First},
        volume={},
        number={},
        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
Abdessalam Hattab1,*, Ali Behloul1
  • 1: LaSTIC laboratory, computer science department, University of Batna 2, 05000, Algeria
*Contact email: a.hattab@univ-batna2.dz

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.