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Many of the commonly used datasets for face recognition development arecollected from the internet without proper user consent. Due to the increasingfocus on privacy in the social and legal frameworks, the use and distributionof these datasets are being restricted and strongly questioned. Thesedatabases, which have a realistically high variability of data per identity,have enabled the success of face recognition models. To build on this successand to align with privacy concerns, synthetic databases, consisting purely ofsynthetic persons, are increasingly being created and used in the developmentof face recognition solutions. In this work, we present a three-playergenerative adversarial network (GAN) framework, namely IDnet, that enables theintegration of identity information into the generation process. The thirdplayer in our IDnet aims at forcing the generator to learn to generateidentity-separable face images. We empirically proved that our IDnet syntheticimages are of higher identity discrimination in comparison to the conventionaltwo-player GAN, while maintaining a realistic intra-identity variation. Wefurther studied the identity link between the authentic identities used totrain the generator and the generated synthetic identities, showing very lowsimilarities between these identities. We demonstrated the applicability of ourIDnet data in training face recognition models by evaluating these models on awide set of face recognition benchmarks. In comparison to the state-of-the-artworks in synthetic-based face recognition, our solution achieved comparableresults to a recent rendering-based approach and outperformed all existingGAN-based approaches. The training code and the synthetic face image datasetare publicly available ( https://github.com/fdbtrs/Synthetic-Face-Recognition).

Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition