  Face plays an important role in human's visual perception, and reconstructing
perceived faces from brain activities is challenging because of its difficulty
in extracting high-level features and maintaining consistency of multiple face
attributes, such as expression, identity, gender, etc. In this study, we
proposed a novel reconstruction framework, which we called Double-Flow GAN,
that can enhance the capability of discriminator and handle imbalances in
images from certain domains that are too easy for generators. We also designed
a pretraining process that uses features extracted from images as conditions
for making it possible to pretrain the conditional reconstruction model from
fMRI in a larger pure image dataset. Moreover, we developed a simple pretrained
model to perform fMRI alignment to alleviate the problem of cross-subject
reconstruction due to the variations of brain structure among different
subjects. We conducted experiments by using our proposed method and
state-of-the-art reconstruction models. Our results demonstrated that our
method showed significant reconstruction performance, outperformed the previous
reconstruction models, and exhibited a good generation ability.
