Our Deep CNN Face Matchers Have Developed Achromatopsia
Aman Bhatta Domingo Mery Haiyu Wu Joyce Annan Micheal C. King Kevin W. Bowyer
Modern deep CNN face matchers are trained on datasets containing color
images. We show that such matchers achieve essentially the same accuracy on the
grayscale or the color version of a set of test images. We then consider
possible causes for deep CNN face matchers ``not seeing color''. Popular
web-scraped face datasets actually have 30 to 60\% of their identities with one
or more grayscale images. We analyze whether this grayscale element in the
training set impacts the accuracy achieved, and conclude that it does not.
Further, we show that even with a 100\% grayscale training set, comparable
accuracy is achieved on color or grayscale test images. Then we show that the
skin region of an individual's images in a web-scraped training set exhibit
significant variation in their mapping to color space. This suggests that
color, at least for web-scraped, in-the-wild face datasets, carries limited
identity-related information for training state-of-the-art matchers. Finally,
we verify that comparable accuracy is achieved from training using
single-channel grayscale images, implying that a larger dataset can be used
within the same memory limit, with a less computationally intensive early
layer.