(Th5). Laila OUANNES

• Thesis topic : Contribution To Face Recognition In a Degraded Environment

• Thesis director : Pr. Najoua ESSOUKRI BEN AMARA

• Co-supervisor : Dr. Anouar BEN KHALIFA.

• Institution : ENISo, University of Sousse.

• Defense date : June 10, 2022

• Abstract : Nowadays, the exploitation of biometrics is becoming crucial in several fields and applications such as access control, border control, and the fight against terrorism. In particular, facial recognition, which is a relatively new technology, has gained prominence around the world in identifying people. However, several challenges inhibit the task of facial recognition such as facial expressions, head pose variations, illumination variations, and partial occlusion. The work developed in this Ph.D. thesis concerns facial recognition under degraded conditions. The first contribution consists in the combination of the k-NN with the KD-Tree for the classification of the interest points provided by the SURF descriptor. The new classifier ensures the performance of our identification system. The second contribution consists in comparing hand-crafted features with learned features provided by the pre-trained deep learning models such as VGG-19 and Inception-v3. These have exceeded hand-crafted features provided by the usual descriptors such as the HOG. The development of a Siamese network based on the Inception-v3 pre-trained model and using the contrastive loss function is the subject of the third contribution. Indeed, Siamese networks have proven their efficiency to overcome the problems of degraded conditions and the necessity of huge datasets that need deep learning models. Our fourth contribution is manifested by an investigation of the impact of occlusion on facial recognition. In this context, we have resorted to de-occlusion and reconstruction of occluded faces. First, we detect the faces using our detector that combines the HSV skin color and the Viola & Jones detector. This combination reinforces the Viola & Jones detector against partial occlusion and head pose variations. Then, for the reconstruction, we use two methods: the Laplacian pyramid blending and CycleGANs. The comparison between reconstructed, de-occluded, and occluded faces shows that partial occlusion inhibits people identification and that reconstruction improves facial recognition performance. All these approaches are evaluated on two public databases: EKFD and IST-EURECOM LFFD, which provide the different challenges of facial recognition. The obtained experimental results show the robustness of the proposed approaches in terms of precision and efficiency.

• Key words : Facial recognition, degraded environment, occlusion, hand-crafted features, learned features, Siamese networks, face detection, face reconstruction, deep learning, pretrained models, Laplacian pyramid blending, CycleGANs.

• Publications : This thesis led to the publication of the following papers :

(C34). Laila Ouannes, Anouar Ben Khalifa, Najoua Essoukri Ben Amara, Siamese Network for Face Recognition in Degraded Conditions Siamese, 6th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP'2022), pp. 1-6, 2022, Hybrid Moncton (Canada)-Sfax (Tunisia). DOI: https://doi.org/10.1109/ATSIP55956.2022.9805878.

(J17). L. Ouannes, Anouar Ben Khalifa, Najoua Essoukri Ben Amara, Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions, Traitement du Signal, Vol. 38, No. 3, pp. 573-585, June 2021. DOI : https://doi.org/10.18280/ts.380305 . Quartile: Q3, IF= 2.589.

(C23). L. Ouannes, Anouar Ben Khalifa, N. Essoukri Ben Amara, Facial Recognition in Degraded Conditions Using Local Interest Points, 17th IEEE International Multi-Conference on Systems, Signals and Devices (SSD’20) , 20-23 july 2020, pp. 404-409 Sfax-Tunisia. DOI : https://doi.org/10.1109/SSD49366.2020.9364124

(C22). Laila Ouannes Nasr, Anouar Ben Khalifa, Najoua Essoukri Ben Amara, Deep Learning vs Hand-Crafted Features for Face Recognition under Uncontrolled Conditions, IEEE International Conference on Signal, Control and Communication (SCC’19), pp 1-6, 2019. DOI: https://doi.org/10.1109/SCC47175.2019.9116159.