Okay, but my suggestion is that this mode of empirical verification just isn't good enough. People have been using this obvious method for decades, and we still can't solve face detection (face recognition is presumably much harder than mere detection). This implies we need a non-obvious method of empirical evaluation.

Okay, but non-"whatever scientists are currently doing" is not an epistemology! You need to say what specific alternate you believe would be better, and you haven't done so. Instead, you've just made broad, sweeping generalizations about how foolish most scientists are, while constantly delaying the revelation of the superior method you think they should be using.

Please, please, just get to the point.

As for facial recognition, the error was in believing that it should be simple to explain what we're doing to a blank slate. Our evolutionary history includes billions of years of friend-or-foe, kin-or-nonkin identification, which means the algorithm that analyzes images for faces will be labyrinthine and involve a bunch of hammered-together pieces. But I don't see anything you've proposed that would lead to a faster solution of this problem; just casual dismissals that everyone's doing it wrong because they use fancy math that just can't be right.

FWIW, the best way to reverse-engineer a kind of cognition is to see what it gets wrong so you know what heuristics it's using. For facial recognition, that means optical illusions regarding faces. For example, look at this.

The bottom two images are the same, but flipped. Yet one face looks thinner than the other. There's a clue worth looking at.

The top two images are upside-down pictures of Margaret Thatcher that don't seem all that different, but when you flip it over to see them right-side up on your photo-viewing tool (and consider this your WARNING), one looks hideously deformed. There's another clue to look at.

Now, how would I have known to do that from the new, great epistemology you're proposing?