Active vs. Passive Liveness Detection: Which Should You Use?
Compare active and passive liveness detection approaches — understand the trade-offs between security, user experience, and implementation complexity.
Two Approaches to Liveness
Both active and passive liveness detection solve the same problem: ensuring a face belongs to a real, live person. But they take very different approaches.
Passive Liveness Detection
How it works: Analyzes a single image using AI to detect signs of spoofing — texture analysis, reflection patterns, depth cues, and other signals that distinguish real skin from screens and paper.
Pros:
Cons:
Learn more in our deep dive on passive liveness.
Active Liveness Detection
How it works: Generates a random set of instructions (look up, look left, etc.) and asks the user to perform them on video. The system verifies the movements were performed in the correct sequence.
Pros:
Cons:
Step 1: Request a challenge
curl -X POST "https://faceapi.arsa.technology/api/v1/face_liveness_active/request?difficulty=medium" \
-H "x-key-secret: YOUR_API_KEY"
Response: {"instructions": ["up", "left"], "job_uuid": "..."}
Step 2: Record video of user performing movements, then submit
curl -X POST "https://faceapi.arsa.technology/api/v1/face_liveness_active/submit?job_uuid=UUID" \
-H "x-key-secret: YOUR_API_KEY" \
-F "video=@recording.mp4"
Which Should You Choose?
| Scenario | Recommended |
|----------|------------|
| Quick onboarding, low risk | Passive only |
| KYC/banking, medium risk | Passive + selfie-to-ID comparison |
| High-security, regulated | Active + passive combined |
| Exam proctoring | Active (continuous) |
ARSA Offers Both
ARSA Face Recognition API includes both passive and active liveness detection. Start your free trial or view the documentation.