  Face recognition has been used more and more in real world applications in
recent years. However, when the skin color bias is coupled with intra-personal
variations like harsh illumination, the face recognition task is more likely to
fail, even during human inspection. Face normalization methods try to deal with
such challenges by removing intra-personal variations from an input image while
keeping the identity the same. However, most face normalization methods can
only remove one or two variations and ignore dataset biases such as skin color
bias. The outputs of many face normalization methods are also not realistic to
human observers. In this work, a style based face normalization model
(StyleFNM) is proposed to remove most intra-personal variations including large
changes in pose, bad or harsh illumination, low resolution, blur, facial
expressions, and accessories like sunglasses among others. The dataset bias is
also dealt with in this paper by controlling a pretrained GAN to generate a
balanced dataset of passport-like images. The experimental results show that
StyleFNM can generate more realistic outputs and can improve significantly the
accuracy and fairness of face recognition systems.
