Abstract
Face recognition systems has reached a level of maturity which makes it applicable in many applications. However, despite these recent results, the precision of face recognition systems still needs to be improved, especially with regard to facial expression and pose variation. Unlike existing methods that mostly operate on existed databases for recognition and identification, we present a new technique of facial expression neutralisation based on synthesizing realistic neutral images using Generative Adversarial Networks (GANs). In this article, we want to propose a new optimization technique called Ranger to be applied in the GANnotation architecture. Promising results are obtained on the RaFD dataset leading to improvements in the facial recognition system.
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Wiem, G., Ali, D. (2021). Automatic Facial Expression Neutralisation Using Generative Adversarial Network. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_1
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DOI: https://doi.org/10.1007/978-3-030-80568-5_1
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