Automatic Synthesis Approach for Unconstrained Face Images Based on Generic 3D Shape Model

  • Hamid OuananEmail author
  • Mohammed Ouanan
  • Brahim Aksasse
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1076)


Faces captured in unconstrained conditions represent a myriad of challenges like occlusions, non-frontal poses, etc. Compared to the other variations, pose variation is one of the most difficult problems in face recognition. To this end, we propose a new automatic synthesis approach of frontal facing view of unconstrained face images which are based on the use of a generic 3D shape model and accurate localization of facial feature points. On another hand, we have used the same approach as data augmentation technique for enriching a small dataset with important facial appearance variations by manipulating the faces that it contains so as to improve the convolutional neural network (CNN) performances and avoid overfitting. This is done by using a camera model to generate multiple views covering possible poses synthesized from the 3D face model used.


Unconstrained conditions 3D face model Pose estimation Overfitting Deep learning 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.National School of Applied SciencesSultan Moulay Slimane UniversityBeni MellalMorocco
  2. 2.Dept. of Computer Science, M2I Laboratory, Faculty of Science and TechniquesMoulay Ismail UniversityErrachidiaMorocco

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