Jared Andrews

A while back I discovered the CV Dazzle project which provides fashionable guidelines for subverting face detection:

A key component of these styles is the use of hair to obscure the face and trick the detection software. I have been growing my hair for a couple years now and I wanted to see how effectiviely I could thwart face detection with only the hair on my head and inspiration from the CV Dazzle guidelines. I was curious if the more extreme aspects of CV Dazzle looks were truly required to trick cameras or if just long, unconventionally placed hair would be enough.

I tested against three facial recognition tools: OpenCV, Betaface and Face++. OpenCV is the most naive of these tools and it was only able to detect my face in the control portraits. OpenCV is the system that CV Dazzle derives its name from and was used to determine what an "anti-face" looks like.

Betaface and Face++ are much smarter than OpenCV and quite difficult to trick. While Betaface and Face++ were succesful in detecting a face, they were only somewhat succesful at extract metadata from the face being analyzed. I would be interested in seeing a test of these face detection tools against the original models for CV Dazzle.

Regular, hair down

	Detected	Age	Gender	Race	Expression
OpenCV	True
Betaface	True	36	Male	White	Neutral
Face++	True	36	Male	White	Neutral

Regular, hair up

	Detected	Age	Gender	Race	Expression
OpenCV	True
Betaface	True	24	Male	White	Neutral
Face++	True	32	Male	White	Neutral

Moptop

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	False
Face++	False

This is the only hairstyle that all three face detection tools were unable to detect. This is also the only image that doesn't contain any trace of my eyes which is probably why it tricked the more sophisticated tools in addition to OpenCV which failed to detect faces when only a single eye was shown. Unfortunately it is really hard to see while rocking this hairstyle.

Single Eye

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	True	46	Female	White	Neutral
Face++	True	29	Male	Asian	Sadness

Rabbit Foot Devilock

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	True	31	Male	Asian	Neutral
Face++	True	30	Male	White	Neutral

Dancing Jellyfish

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	True	33	Male	Asian	Neutral
Face++	True	29	Male	Asian	Neutral

Predator

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	True	51	Male	Mideast	Neutral
Face++	True	32	Male	White	Neutral

Emo-Nelly

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	False
Face++	True	31	Male	White	Sadness

Emo

	Detected	Age	Gender	Race	Expression
OpenCV	False
Betaface	True	46	Female	White	Neutral
Face++	True	26	Male	White	Neutral

While OpenCV consistently fails and Betaface/Face++ consistently succeed it is interesting to examine some of the metadata they report.

	Actual	Betaface	Face++
Age	26	33.3	29.6
Male	9 / 9 (100%)	5 / 9 (56%)	8 / 9 (89%)
White	9 / 9 (100%)	4 / 9 (44%)	6 / 9 (67%)

Despite my hair, the data is fairly accurate. While many projects may use the open source OpenCV face detection tools, it's clear there are at least two face detection tools on the market that have no problem seeing past my wildly arranged hair. I guess I will need to pull out some hair dye if I want to stay undetectable.