Experimental results show that the proposed facial emotion synthesis method outperforms existing methods in terms of both qualitative performance and quantitative performance of expression recognition. The variation-realistic discriminator determines whether the multi-resolution facial image has natural variation or not. The photo-realistic discriminator in the multi-level critic network determines whether the multi-resolution facial image generated from the latent feature of the multi-level decoding module is photo-realistic or not. The multi-level critic network consists of two discriminators, photo-realistic discriminator and variation-realistic discriminator. A proposed multi-level decoder and multi-level critic network help the generator to produce a photo-realistic and variation-realistic facial image in generative adversarial learning. We devise a new facial emotion generator containing the proposed multi-level decoder to synthesize facial image with a desired variation. In this paper, we propose photo-realistic facial emotion synthesis by using a novel multi-level critic network with multi-level generative model.
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