Synthetic Face Generation Through Eyes-to-Face Inpainting
Ahmad Hassanpour, Sayed Amir Mousavi Mobarakeh, Amir Etefaghi Daryani, Raghavendra Ramachandra, Bian Yang · 2023
This study introduces a new technique for generating synthetic faces using eyes-to-face inpainting methods. The proposed method can synthesize a face image using a combination of the eyes of two different individuals and use it as an input for inpainting, demonstrating its vast potential for various applications in biometrics. Despite minor biases in age and gender, our method proved effective in training reliable age- and gender-detection models using the generated datasets. We also addressed the challenge of training face recognition models using synthetic datasets, and the results demonstrated satisfactory accuracy across four benchmark face recognition datasets. This method could be particularly beneficial for underrepresented groups, for whom there is a scarcity of face samples in biometric datasets.