thwart facial detection with a couple clicks.

The Face Dazzler web application allows you a degree of protection against facial recognition technology online.

Facial recognition technology is pervasive, from street-level security cameras to friends tagging us on Facebook. It's also invasive – when our face is recognized, so is our identity.

One way around this is to avoid having our face recognized as a face in the first place.

Examples of hair and makeup styles that thwart facial detection, from CVDazzle

experiment with facial detection in real time

Detect and dazzle your face
Experiment with CV Dazzle methods
that circumvent facial detection

use webcam

check for faces in photos

After you experiment with digital camouflage,
Check to see if you've successfully thwarted facial detection

upload a photo

learn more

Learn more about surveillance, facial recognition and facial detection
Including more about this site

learn more

use facial detection with your webcam

Tips and examples to experiment with:
Examples of CV Dazzle designs that thwart facial detection

Makeup: Avoid enhancers; they amplify key facial features. This makes your face easier to detect. Instead apply makeup that contrasts with your skin tone in unusual tones and directions: light colors on dark skin, dark colors on light skin.

Nose Bridge: Partially obscure the nose-bridge area: The region where the nose, eyes, and forehead intersect is a key facial feature. This is especially effective against common face detection algorithms.

Eyes: Partially obscure one of the ocular regions: The position and darkness of eyes is a key facial feature.

Masks: Avoid wearing masks as they are illegal in some cities. Instead of concealing your face, modify the contrast, tonal gradients, and spatial relationship of dark and light areas using hair, makeup, and/or unique fashion accessories.

Head: Obscuring the elliptical shape of a head can also improve your ability to block face detection.

Asymmetry: Facial-recognition algorithms expect symmetry between the left and right sides of the face. By developing an asymmetrical look, you may decrease your probability of being detected.

use facial detection on a photo

Face detected!

No face detected

A facial detection algorithm at work

Each black and white patch represents a feature that the algorithm hunts for in the image.

more about facial detection

OpenCV Face Detection: Visualized from Adam Harvey on Vimeo.

This video visualizes the detection process of OpenCV's face detector. The algorithm uses the Viola Jones method of calculating the integral image and then performing some calculations on all the areas defined by the black and white rectangles to analyze the differences between the dark and light regions of a face. The sub-window (in red) is scanned across the image at various scales to detect if there is a potential face within the window. If not, it continues scanning. If it passes all stages in the cascade file, it is marked with a red rectangle. But this does not yet confirm a face. In the post-processing stage all the potential faces are checked for overlaps. Typically, 2 or 3 overlapping rectangles are required to confirm a face. Loner rectangles are rejected as false-positives.
This visualization was done as part of the documentation for CV Dazzle, camouflage from face detection.

About this project

The Face Dazzler For Web application was built using tracking.js, a Javascript library that enables facial detection. The concept for this application is based on the work of Adam Harvey, a Brooklyn-based artist who explores the impacts of surveillance technologies. His project CV Dazzle explored how fashion can be used as camouflage from face-detection technology.

The name is derived from a type of World War I naval camouflage called Dazzle, which used cubist-inspired designs to break apart the visual continuity of a battleship and conceal its orientation and size. Likewise, CV Dazzle uses avant-garde hairstyling and makeup designs to break apart the continuity of a face. Since facial-recognition algorithms rely on the identification and spatial relationship of key facial features, like symmetry and tonal contours, one can block detection by creating an “anti-face”.


Viola-Jones algorithm commonly used for facial detection
CV Dazzle
Adam Harvey
Facial detection
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