FNR-GAN: Face Normalization and Recognition with Generative Adversarial Networks - Université d'Évry Access content directly
Conference Papers Year : 2022

FNR-GAN: Face Normalization and Recognition with Generative Adversarial Networks

Abstract

The normalization of faces in the wild is an interesting process which can improve face recognition performances and avoid the complex computation of face generation in cross different variations. In this paper, we propose a multi-objective approach that generates and recognizes normalized faces while preserving their identities. An unsupervised Face Normalization and Recognition framework using discriminant normalized features is presented. This latter is based on an optimized combination of Generative Adversarial Network (GAN) generators and Convolutional Neural Network (CNN) classifiers. The main power of our approach is to generate optimized features representing normalized faces finding a trade-off between improving identity preservation and minimizing the architecture complexity. Additionally, it can be adapted to impaired and unlabeled datasets which can respond to real-world face variations and available data. Experimental results show that the proposed method outperforms other models on face normalization and achieves state-of-the-art frontal-frontal face verification in CFP protocol and face recognition in LFW. The code and results are available at github/FNR-GAN.
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Dates and versions

hal-04005281 , version 1 (26-02-2023)

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Cite

Amina Kammoun, Rim Slama, Hedi Tabia, Tarek Ouni, Mohamed Abid. FNR-GAN: Face Normalization and Recognition with Generative Adversarial Networks. 37th International Conference on Image and Vision Computing New Zealand (IVCNZ 2022), Nov 2022, Aukland, New Zealand. pp.131--143, ⟨10.1007/978-3-031-25825-1_10⟩. ⟨hal-04005281⟩
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