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It is well known that deep learning approaches to face recognition and faciallandmark detection suffer from biases in modern training datasets. In thiswork, we propose to use synthetic face images to reduce the negative effects ofdataset biases on these tasks. Using a 3D morphable face model, we generatelarge amounts of synthetic face images with full control over facial shape andcolor, pose, illumination, and background. With a series of experiments, weextensively test the effects of priming deep nets by pre-training them withsynthetic faces. We observe the following positive effects for face recognitionand facial landmark detection tasks: 1) Priming with synthetic face imagesimproves the performance consistently across all benchmarks because it reducesthe negative effects of biases in the training data. 2) Traditional approachesfor reducing the damage of dataset bias, such as data augmentation and transferlearning, are less effective than training with synthetic faces. 3) Usingsynthetic data, we can reduce the size of real-world datasets by 75% for facerecognition and by 50% for facial landmark detection while maintainingperformance. Thus, offering a means to focus the data collection process onless but higher quality data.

Can Synthetic Faces Undo the Damage of Dataset Bias to Face Recognition and Facial Landmark Detection?