From the DIS Magazine story “How to Hide from Machines“
Test results of Dis Magazine’s style against OpenCV
First test pattern against OpenCV
Test results of first camouflage design pattern against OpenCV. Hair styling by Pia Vivas
From the DIS Magazine story “How to Hide from Machines“
From the DIS Magazine story “How to Hide from Machines“
From the DIS Magazine story “How to Hide from Machines“
From the DIS Magazine story “How to Hide from Machines“
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.
- Images with a red square tested positive, a face was found
- Images without a red square tested negative, no face was found
- Images under the section “TEST PATTERNS” are made according to results of the Haar deconstruction
- Images under “RANDOM PATTERNS” are random doodles made without the anti-face detection patterns in mind
- Images underneath the “NO PATTERNS” heading are left untouched to show that the face detection works well on simple line drawings
- Line drawings are from Figure Drawing for Fashion Design
Visualization of a Haar Cascade profile for OpenCV face detection. The face detection algorihtm looks for the difference between the light and dark regions and determines whether it is within the threshold of a face pattern. Each cascade contains over 20 stages. Shown here is stage 3. Each stage becomes more discerning so that non-faces are quickly rejected in the beginning of the image scan.