Group photo as profile picture
Four faces detected where platform rules require exactly one.
Face 1
Face 2
Face 3
Face 4
Face detection for user-generated content
Count faces, estimate age, and detect gender in user-generated images, each with confidence scores. Lasso's pipeline applies your platform rules and uses context-aware AI to handle edge cases.
Age estimation flags potential minor
Face detection estimates age range 14–16 on a platform requiring 18+.
Age: 14–16 · 89%Face obstructed by sunglasses
Platform requires full face visible — AI Moderator flags the obstruction.
SunglassesNo human face detected
Pet photo uploaded as profile picture — face count returns zero.
Faces: 0
Face detection: four layers from scan to moderation decision
1ML detection
Image enters the pipeline. ML layer detects faces, counts them, estimates age range and gender.
It returns confidence scores in under 200 milliseconds.
See Lasso in action
Face 1
Face 2
Face 3
Face 4
Face Detection
4 faces detected
Face count
4 facesmultiple
Age range
22–28 yearsadult
Gender
2 male, 2 femalemixed
Detection confidence94%
2Custom rules
Your rules applied to face detection data.
Dating: require exactly 1 face, confidence above 80%. Adult: flag any face estimated under 18. Marketplace: require face in seller profile photo.
See Lasso in actionLayer 1 Output
1Face count: 4
2Age range: 22–28
3Gender: detected
Custom Rules
BlockProfile photo validation
Face count must equal 1
Face confidence above 80%
Age range 18 or older
Face must be present
Blocked
Multiple faces in profile photo
3AI Moderator
Context-aware moderation handles what rules cannot check.
Is the full face visible? Are they wearing sunglasses? Is this a live photo or someone holding up a screen? AI Moderator checks whatever your platform requires.
See Lasso in action
Face obstruction detected
AI Moderator
Analyzing
Face detected
1 face, female, age 25–30
Visibility check
Eyes not visible — sunglasses obstruct
Policy match
Full face required for dating profiles
Assessment
Obstruction violation — confidence 74%
Flagged for review
Confidence below threshold
4Human review
Edge cases reach your team with full context.
Reviewers see the flagged photo, face detection data, the AI Moderator assessment, and confidence scores. Each decision they make feeds back, so accuracy improves over time.
See Lasso in actionQueued for review
AI Moderator reasoning
Face detected but eyes obscured by sunglasses. Platform requires full face visibility.
Human Review1 of 2
Face data
1 face · Female · Age 25–30
AI assessment
Obstruction: sunglasses — may be prescription lenses
Confidence
Confidence74%
Below auto-action threshold (90%)
This decision will train the AI for future similar content
Detection capabilities for face detection.
Detect whether a face is present in an uploaded image. Returns face count with bounding box coordinates and confidence score per face. Profile enforcement starts with counting.
Uploaded Photos
1 face
3 faces
2 faces
0 faces
Detection Results
photo_001.jpg
1 face · 98.2%
photo_002.jpg
3 faces · 96.7%
photo_003.jpg
2 faces · 97.1%
photo_004.jpg
0 faces
Faces Detected
6faces
Estimate apparent age range from facial features. Returns a range (e.g., 14 to 16, 25 to 30) with a confidence score. Use it for minor protection, age-gating, and compliance with DSA, OSA, and COPPA.
Enforce any face requirement with context-aware moderation: full face visible, no sunglasses, no masks, liveness verification. Describe what your platform needs, and the AI Moderator checks it on every upload.
Uploaded Photos
1 face
3 faces
2 faces
0 faces
Detection Results
photo_001.jpg
1 face · 98.2%
photo_002.jpg
3 faces · 96.7%
photo_003.jpg
2 faces · 97.1%
photo_004.jpg
0 faces
Faces Detected
6faces
Lasso: next-gen AI content moderation
99.9%
On autopilot
Face detection runs in the four-layer pipeline. From ML scan to human review, 99.9% of decisions are automated.
Not just an API. A complete moderation platform.
Face detection includes detection, rule enforcement, context-aware AI, human review workflows, and a feedback loop. Most face-detection vendors stop at an API.
A moderation partner that works alongside your team.
We help you configure face detection rules, optimize thresholds, and handle edge cases together, beyond documentation and a support ticket.
★★★★★
4.9
Highest rated in content moderation.
Every platform has different face requirements. Lasso enforces them.
Dating
Profile validation for dating platforms
- Face detection requires exactly one face. Group shots rejected automatically.
- Age estimation flags potential minors. Confidence scores determine next steps.
- Context-aware moderation enforces clear, unobstructed face visibility.
Adult Entertainment
Age verification for adult platforms
- Face detection on every upload. Age range estimated with confidence scores.
- Content from users estimated under 18 blocked automatically.
- Supports DSA and OSA compliance requirements.
Marketplaces
Seller profile verification for trust
- Face detection requires a real face in every seller profile photo.
- Age estimation supports age-restricted categories.
- Context-aware moderation validates photo quality.
FAQs
Face detection, answered
Face detection finds human faces in user-uploaded images and returns structured data about each one: a face count, bounding box coordinates, an estimated age range, and an estimated gender, each with a confidence score. Platforms use it to validate profile photos, age-gate uploads, and verify seller images. It runs through Lasso's four-layer pipeline rather than as a standalone API.
Every image moves through four layers. ML detection finds and counts faces and estimates age and gender with confidence scores. Custom rules then apply your platform policy, such as requiring exactly one face. The AI Moderator handles context a rule cannot check, like sunglasses or liveness, and the roughly 1% of edge cases that remain go to human review with full context. Each human decision feeds back to improve accuracy over time.
No. Face detection locates faces and describes attributes such as count, age range, and gender. It does not match a face to a named individual or a database, which is face recognition, a separate capability. If your platform needs to identify or match people, that is handled by a different feature.
Yes. Lasso is GDPR-compliant and keeps audit logs of moderation decisions for accountability and DSA reporting. Face detection describes attributes in an image rather than building a biometric identity profile of a named person. For specifics on how facial data is processed and retained, our team can walk you through it on a demo.
Face detection returns: face count (how many faces), bounding box coordinates per face, estimated age range with confidence score, estimated gender with confidence score. Available via API and in the moderation dashboard.
Age estimation returns a range, not a single number. A wider range indicates lower confidence. Platforms set their own confidence thresholds.
Context-aware moderation can check any visual face requirement: full face visible, no sunglasses, no masks or coverings, no heavy filters or effects, liveness (real person vs photo of a photo), proper face angle and framing. Describe what your platform needs. The AI Moderator enforces it.
Face detection supports compliance through age estimation. DSA requires platforms to protect minors from harmful content. OSA requires age assurance for adult platforms. COPPA protects children under 13. Lasso's age estimation flags potential minors based on facial features, routing them to additional verification.
Dating platforms use face detection for profile validation (one clear face, no sunglasses, no group photos) and age estimation for safety. Adult entertainment platforms use age estimation for DSA and OSA compliance. Marketplaces use face detection for seller profile verification and trust.
See what face detection catches in your users' uploads
Profile validation, age estimation, and context-aware enforcement run in one pipeline. Book a demo to see it live.
Book a demo
Lasso Moderation
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