Automatically detect faces in photos and blur them for privacy. Uses computer vision for accurate face finding.
Snipinsta uses Haar cascade classifiers — a machine learning approach that scans images for face-like patterns. The algorithm evaluates thousands of features (eye spacing, nose position, face proportions) in overlapping windows across the image. Detected face regions are highlighted with bounding boxes and can be automatically blurred or pixelated.
Under GDPR and similar privacy laws, publishing identifiable photos of people without consent can result in fines. Automatically detecting and blurring faces in street photography, event documentation, real estate listings, and security footage helps organizations comply with data protection regulations.
Gaussian blur produces a smooth, professional look suitable for news media and corporate content. Pixelation (mosaic) is a stronger anonymization method, making it harder to reverse-engineer the original face. For maximum privacy protection, use pixelation with a block size of 15+ pixels.
For best results: use well-lit frontal photos, ensure faces are at least 50×50 pixels, and avoid extreme angles. If some faces aren't detected, try lowering the sensitivity threshold (min neighbors setting). Group photos with 10+ faces may need adjusted scale factor settings. Side profiles and heavily occluded faces are harder to detect with cascade classifiers.