The lack of ethnic diversity in data has been a limiting factor of face recognition techniques in the literature. This is particularly the case for children where data samples are scarce and presents a challenge when seeking to adapt machine vision algorithms that are trained on adult data to work on children. This work proposes the utilization of image-to-image transformation to synthesize data of different races and thus adjust the ethnicity of children's face data. We consider ethnicity as a style and compare three different Image-to-Image neural network based methods, specifically pix2pix, CycleGAN, and CUT networks to implement Caucasian child data and Asian child data conversion. Experimental validation results on synthetic data demonstrate the feasibility of using image-to-image transformation methods to generate various synthetic child data samples with broader ethnic diversity.

翻译：数据中种族多样性的缺乏一直是文献中人脸识别技术的限制因素。对于儿童数据样本稀少的情况尤其如此，并且在对基于成人数据训练的机器视觉算法进行调整以适用于儿童时提出了挑战。本研究提出利用图像到图像变换来合成不同种族的数据，从而调整儿童人脸数据的种族属性。我们将种族视为一种风格，并比较了三种不同的基于图像到图像神经网络的方法，具体为pix2pix、CycleGAN和CUT网络，以实现高加索儿童数据与亚洲儿童数据的转换。对合成数据的实验验证结果证明了使用图像到图像变换方法生成具有更广泛种族多样性的各种合成儿童数据样本的可行性。