Researchers at cyber-defense contractor PeopleTec have found that facial-recognition algorithms' focus on specific areas of the face opens the door to subtler surveillance avoidance strategies.
In a pre-print paper titled "Novel AI Camera Camouflage: Face Cloaking Without Full Disguise," David Noever, chief scientist, and Forrest McKee, data scientist, describe their efforts to baffle face recognition systems through the minimal application of makeup and manipulation of image files.
Noever and McKee recount various defenses that have been proposed against facial recognition systems, including CV Dazzle, which creates asymmetries using high-contrast makeup, adversarial attack graphics that confuse algorithms, and Juggalo makeup, which can be used to obscure jaw and cheek detection.
And of course, there are masks, which have the advantage of simplicity and tend to be reasonably effective regardless of the facial recognition algorithm being used.