Les variations de pose et d’expression constituent des limitations importantes à la reconnaissance de visages en deux dimensions. Dans cette thèse, nous proposons d’augmenter la robustesse des algorithmes de reconnaissances faciales aux changements de pose et d’expression. Pour cela, nous proposons d’utiliser un modèle 3D déformable de visage permettant d’isoler les déformations d’identité de celles relatives à l’expression. Plus précisément, étant donné une image de probe avec expression, une nouvelle vue synthétique du visage est générée avec une pose frontale et une expression neutre. Nous présentons deux méthodes de correction de l’expression. La première est basée sur une connaissance a priori dans le but de changer l’expression de l’image vers une expression neutre. La seconde méthode, conçue pour les scénarios de vérification, est basée sur le transfert de l’expression de l’image de référence vers l’image de probe. De nombreuses expérimentations ont montré une amélioration significative des performances et ainsi valider l’apport de nos méthodes. Nous proposons ensuite une extension de ces méthodes pour traiter de la problématique émergente de reconnaissance de visage à partir d’un flux vidéo. Pour finir, nous présentons différents travaux permettant d’améliorer les performances obtenues dans des cas spécifiques et ainsi améliorer les performances générales obtenues grâce à notre méthode. / Expression and pose variations are major challenges for reliable face recognition (FR) in 2D. In this thesis, we aim to endow state of the art face recognition SDKs with robustness to simultaneous facial expression variations and pose changes by using an extended 3D Morphable Model (3DMM) which isolates identity variations from those due to facial expressions. Specifically, given a probe with expression, a novel view of the face is generated where the pose is rectified and the expression neutralized. We present two methods of expression neutralization. The first one uses prior knowledge to infer the neutral expression from an input image. The second method, specifically designed for verification, is based on the transfer of the gallery face expression to the probe. Experiments using rectified and neutralized view with a standard commercial FR SDK on two 2D face databases show significant performance improvement and demonstrates the effectiveness of the proposed approach. Then, we aim to endow the state of the art FR SDKs with the capabilities to recognize faces in videos. Finally, we present different methods for improving biometric performances for specific cases.

[Non communiqué] / In the past decade, 3D statistical face model (3D Morphable Model) has received much attention by both the commercial and public sectors. It can be used for face modeling for photo-realistic personalized 3D avatars and for the application 2D face recognition technique in biometrics. This thesis describes how to achieve an automatic 3D face reconstruction system that could be helpful for building photo-realistic personalized 3D avatars and for 2D face recognition with pose variability. The first systems we propose Combined Active Shape Model for 2D frontal facial landmark location and its application in 2D frontal face recognition in degraded condition. The second proposal is 3D Active Shape Model (3D-ASM) algorithm which is presented to automatically locate facial landmarks from different views. The third contribution is to use biometric data (2D images and 3D scan ground truth) for quantitatively evaluating the 3D face reconstruction. Finally, we address the issue of automatic 2D face recognition across pose using 3D Morphable Model

The steep growth of video-games is demanding a higher amount of characters in the games. The process of generating characters is very expensive and time consuming. Consequently, this process doesn’t cover the current demands and could be optimized by developing a generative model able to synthesize high- quality 3D avatar faces within minutes. This model would result in drastic gains for gaming companies. Therefore, the aim of this project is to implement a model able to generate realistic 3D avatar faces by generating texture and shape when a limited amount of data is given (<1k samples). This type of model is called 3D Morphable Model and it will also learn the correlation between shape and texture in order to generate consistent results. In order to achieve this final model, which is called joint model, individual models for texture and shape are also developed. The three type of models are built upon StyleGAN2-ADA architecture. The final design of the joint model has three discriminators: a joint discriminator to ensure consistency and two individual discriminators to have good quality for shape and texture. This model was inspired from [1]. The experiments show that the best texture model uses the augmentation techniques introduced in StyleGAN2-ADA. The experiments over the joint model prove that having just one discriminator is not enough to generate good quality results. On the other hand, the joint model with three discriminators give good quality and coherent results. In addition, this joint model outperforms the results of the shape model when training the model with the same number of samples, 969 samples. This model shows a promising path for further improvements. / Den kraftiga ökningen av videospel kräver ett större antal karaktärer i spelen. Processen att skapa karaktärer är mycket dyr och tidskrävande. Denna process täcker därför inte den nuvarande efterfrågan och skulle kunna optimeras genom att utveckla en generativ modell som kan syntetisera högkvalitativa 3D-avataransikten av hög kvalitet på några minuter. Denna modell skulle leda till drastiska vinster för spelföretagen. Syftet med detta projekt är därför att implementera en modell som kan generera realistiska 3D-avataransikten genom att generera textur och form när en begränsad mängd data ges (<1k samplingar). Denna typ av modell kallas 3D Morphable Model och den kommer också att lära sig korrelationen mellan form och textur för att generera konsekventa resultat. För att uppnå denna slutliga modell, som kallas gemensam modell, utvecklas också individuella modeller för textur och form. De tre typerna av modeller bygger på StyleGAN2- ADA-arkitekturen. Den slutliga utformningen av den gemensamma modellen har tre diskriminatorer: en gemensam diskriminator för att säkerställa konsistens och två individuella diskriminatorer för att uppnå god kvalitet för form och textur. Denna modell har inspirerats av [1]. Experimenten visar att den bästa texturmodellen använder de förstärkningstekniker som infördes i StyleGAN2-ADA. Experimenten med den gemensamma modellen visar att det inte räcker med bara en diskriminator för att generera resultat av god kvalitet. Å andra sidan ger den gemensamma modellen med tre diskriminatorer bra kvalitet och sammanhängande resultat. Dessutom överträffar denna gemensamma modell resultaten från formmodellen när modellen tränas med samma antal prov, 969 prov. Denna modell visar en lovande väg för ytterligare förbättringar.