Unsupervised Face Synthesis Based on Human Traits

This paper presents a strategy to synthesize face images based on human traits. Specifically, the strategy allows synthesizing face images with similar age, gender, and ethnicity, after discovering groups of people with similar facial features. Our synthesizer is based on unsupervised learning and is capable to generate realistic faces. Our experiments reveal that grouping the training samples according to their similarity can lead to more realistic face images while having semantic control over the synthesis. The proposed strategy achieves competitive performance compared to the state-of-the-art and outperforms the baseline in terms of the Frechet Inception Distance.