  With the increased accuracy of modern computer vision technology, many access
control systems are equipped with face recognition functions for faster
identification. In order to maintain high recognition accuracy, it is necessary
to keep the face database up-to-date. However, it is impractical to collect the
latest facial picture of the system's user through human effort. Thus, we
propose a bottom-up training method for our proposed network to address this
challenge. Essentially, our proposed network is a translation pipeline that
cascades two CycleGAN blocks (a widely used unpaired image-to-image translation
generative adversarial network) called BiTrackGAN. By bottom-up training, it
induces an ideal intermediate state between these two CycleGAN blocks, namely
the constraint mechanism. Experimental results show that BiTrackGAN achieves
more reasonable and diverse cross-age facial synthesis than other
CycleGAN-related methods. As far as we know, it is a novel and effective
constraint mechanism for more reason and accurate aging synthesis through the
CycleGAN approach.
