Thanks for the answer! I was thinking more along the lines of data augmentation. All in all, I don't think microexpressions alone are a good feature, from your

Thanks for the answer! I was thinking more along the lines of data augmentation. All in all, I don't think microexpressions alone are a good feature, from your description it's clear that they're problematic on a number of fronts. The article also mentions microexpressions as the basis for this new system, and that they worked on a slow universe of 30 people, with ~76% accuracy. What remains to be seen is if the new, larger deployment will work better. Also, they may be working more focused on the low stakes lies rather than the high stakes. What I was thinking that "more compute power" could bring to the table would be deeper and more complex models, faster training algorithms[0] and data augmentation. But then again, all that relies on having good data to begin with. [0] I worked with neural nets in the past (~10 years, I pretty much left the field right before the deep learning big bang), and coming back to the field I'm pretty much amazed at how training a 7 layer deep MLP used to take a few minutes on CPU back in 2010, and now can be done in seconds (still on CPU). And while there's a part of microarchitectural improvement to account for, that could at best be a 5x speedup -- the rest is owed to the newer training algorithms. Once again, thanks for your answer!