Facial Expression Intelligence – Not Just Emotion Labels
Real-time emotion, attention, and micro-expression data from a single API.
FacialProof converts facial movement and gaze into measurable behavioral signals that your software can act on instantly, utilizing micro-expressions with a 468-point 3D model to deliver structured behavioral data in milliseconds.
👁️ Facial recognition
Choose a demo portrait or capture a new photo, then review detections and explore the API request builder.
Reference image
Use a preset below or open the camera. Faces are detected on the image you select.
Demo faces
Results & API
Live face data from Human.js on your image. Request / Response tabs are a sample API explorer only.
{
"success": true,
"faces_detected": 3,
"faces": [
{
"id": 1,
"confidence": 0.95,
"bounding_box": {"x": 150, "y": 200, "width": 300, "height": 350},
"age": "27-29 years old",
"emotion": "Happy",
"gender": "Female"
},
{
"id": 2,
"confidence": 0.92,
"bounding_box": {"x": 500, "y": 180, "width": 280, "height": 320},
"age": "25-27 years old",
"emotion": "Neutral",
"gender": "Male"
},
{
"id": 3,
"confidence": 0.88,
"bounding_box": {"x": 800, "y": 250, "width": 200, "height": 250},
"age": "6-8 years old",
"emotion": "Happy",
"gender": "Male"
}
]
}
🔒 Liveness & ID verification
Upload your ID, start the camera, then follow the on-screen steps to verify it’s really you.
ID card
Use a clear photo of your ID card. The face on the card will be compared to your live camera feed. You can also drag and drop an image here.
Drop ID image here
or
Live camera
Start the camera first. You’ll match your face to the ID, then complete head turns and the number challenge.
Verification results
🎭 Facial Emotion Recognition
Upload an image or use real-time camera for face shape analysis using advanced AI
Drop image here
or
Face Shape
Personal Attributes
Face Measurements
Detection Info
🎤 Speech emotion recognition
Record a short sample (~3 seconds). We’ll show waveform, spectrum, and predicted emotions.
Go beyond identity • Decode intent, mood, and engagement • SDKs for Python, JS, Mobile
Turn Human Expressions into Actionable Insights
Most AI systems identify who a person is. FacialProof tells you how they feel, moment by moment.
Our Facial Emotion Recognition API uses advanced affective computing and a high-density 3D facial model to extract real-time sentiment, attention, and expression data from video or images.
Real-Time Sentiment AI
Instantly detect core emotional states and their intensity as they change, ideal for live interactions, feedback loops, and adaptive UX.
Micro-Expression Detection
Capture involuntary facial reactions that last only milliseconds, often revealing true responses before conscious control kicks in.
Attention & Gaze Tracking
Understand exactly where users look, when focus drops, and what visually engages them most.
Receive structured emotional metrics (not vague labels) that can be logged, scored, and acted on programmatically.
Advanced Facial Expression Recognition – Built on Precision
FacialProof is built on a 468-point 3D facial landmark mesh, enabling ultra-granular tracking of facial muscle activity in real time.
This approach aligns with the Facial Action Coding System (FACS), the scientific standard developed from the research of Paul Ekman.
Real-Time Emotion & Mood Detection
Detect the full emotional spectrum — including neutral and mixed states — as expressions evolve frame by frame.
Tracked emotional states:
- Happiness
- Sadness
- Anger
- Fear
- Surprise
- Disgust
- Contempt
- Neutral / Transitional states
Each emotion is delivered with confidence and intensity scores, allowing you to quantify engagement instead of relying on binary labels.
Gaze & Attention Tracking API
Understand visual attention, not assumptions.
FacialProof tracks eye direction and fixation patterns to determine where users are looking — and when their attention drifts.
Perfect for:
- Accessibility tools and hands-free interaction systems
- UX & usability research
- Ad creative testing and attention heatmaps
High-Resolution Expression Metrics
Capture subtle facial movements that traditional models miss.
Using high-density landmark tracking, FacialProof detects micro-level facial changes with sub-pixel precision.
Measured expression signals include:
- Brow tilt, raise, and tension
- Eye closure, squinting, and asymmetry
- Mouth opening, compression, and smirks
- Cheek activation and jaw movement
This allows you to distinguish between genuine reactions and socially masked expressions.
Real-World Applications of Expression AI
Measure real emotional response — not opinions.
Market Research & Retail
See how people actually react to ads, products, and experiences in real time. FacialProof reveals when attention peaks, interest drops, or confusion appears — without surveys.
Use it to: optimize creatives, compare variants, and identify emotional friction.
Gaming & Virtual Worlds
Make experiences respond to the player.
Power emotion-aware avatars and NPCs that react to real facial expressions and mood shifts, creating deeper immersion and more believable interactions.
Use it to: drive NPC behavior, mirror expressions, and adapt gameplay dynamically.
Healthcare, Wellness & Education
Detect emotional state early — and act on it.
Monitor stress, engagement, and confusion in telehealth and learning environments without invasive sensors or self-reporting.
Use it to: track mood changes, detect disengagement, and adapt content or care in real time.
Passive vs. Active: The FacialProof Advantage
| Feature | Passive Liveness (Invisible) | Active Liveness (Challenge) |
|---|---|---|
| User Effort | Zero. The user just takes a selfie. | High. User must blink or turn head. |
| Speed | < 1 Second | 5 – 15 Seconds |
| Conversion | 95%+ Higher. No drop-offs. | Lower due to user friction. |
| Best For | Daily logins & Retail Onboarding | High-value transfers & KYC |
Why FacialProof for Facial Expression Intelligence
Turn facial movement into measurable emotion, attention, and intent.
FacialProof delivers high-precision facial expression and sentiment analysis in real time, built for modern, human-aware applications.
What the API Actually Measures
FacialProof doesn’t return vague emotion labels.
It delivers structured behavioral signals you can analyze, score, and act on.
You receive:
- Emotion probabilities (intensity + confidence, not just labels)
- Micro-expression signals revealing subtle, involuntary reactions
- Gaze direction & attention stability over time
- Facial movement metrics (brow, eyes, mouth, jaw, cheeks)
- Session-level emotional timelines for deeper analysis
Developer-First Expression API
One API. Real-Time Behavioral Insight.
Integrate facial emotion, expression, and gaze analysis using a single SDK.
Consistent logic across Python, JavaScript, PHP, Java, Android, and iOS, no fragmented workflows.
# Facial expression & emotion analysis
import facialproof
analysis = facialproof.analyze_frame(
frame,
include=["emotions", "gaze", "expressions"]
)
if analysis.attention.score < 0.4:
adapt_experience()FacialProof vs. Emotion Recognition APIs
When emotional accuracy matters, the differences are structural.
While cloud providers and legacy emotion APIs treat expression analysis as an add-on, FacialProof is built specifically for facial expression, emotion, and attention intelligence.
We focus on signal quality, granularity, and real-time usability, not generic vision tasks.
eature Comparison
| Feature | FacialProof | Microsoft Azure / Google Cloud | Affectiva / Kairos / Hume AI |
|---|---|---|---|
| Primary Focus | Facial expressions & emotion | General vision & identity | Emotion research / multimodal |
| Landmark Density | 468-point 3D mesh | Low-density landmarks | Medium density |
| Emotion Output | Intensity + confidence + timelines | Static emotion labels | Emotion scores |
| Micro-Expressions | Supported | Not supported | Limited |
| Gaze & Attention | Built-in | Limited / separate | Partial |
| Real-Time Use | Optimized for live streams | Image-first pipelines | Often batch / research |
| Integration Model | Simple API, dev-first | Cloud-heavy setup | SDK-centric |
1. More Precise Than Cloud Vision APIs
Emotion detection in Azure Face API and Google Cloud Vision is designed as a secondary feature of general image analysis.
FacialProof is different:
- High-density facial landmark tracking
- Continuous emotion change over time
- Reliable results with head movement and natural expressions
If you need behavioral signal, not surface emotion tags, the gap is clear.
2. Built for Real-Time Emotion, Not Research Pipelines
Platforms like Affectiva, Kairos, and Hume AI pioneered emotion AI — but many are optimized for research, analytics, or multimodal experiments.
FacialProof is built for:
- Live user interaction
- UX, product, and engagement systems
- Real-time emotion + attention feedback
No heavy SDKs. No research tooling overhead.
3. Expression Intelligence, Not Just Emotion Labels
Most emotion APIs answer “what emotion is this?”
FacialProof answers “what changed, how strongly, and for how long?”
That includes:
- Expression intensity
- Micro-movement detection
- Gaze stability and attention loss
- Session-level emotional timelines
This is facial expression intelligence, not generic emotion classification.
FAQs
What is a facial emotion and expression recognition API?
A facial emotion and expression recognition API analyzes facial movements to identify emotional states, expression intensity, and attention signals in real time. Instead of static labels, modern APIs like FacialProof measure how expressions change over time, capturing emotion strength, micro-expressions, and gaze behavior from video or images.
How is facial expression recognition different from basic emotion detection?
Basic emotion detection assigns simple labels such as “happy” or “sad.” Facial expression recognition goes deeper by tracking individual facial muscle movements, expression intensity, and transitions. This allows developers to understand how strongly someone reacts, when the reaction occurs, and whether it is genuine or masked.
What makes a good emotion recognition API?
High-density facial landmarks, real-time speed, gaze tracking, and structured outputs that can be used programmatically.
Can facial emotion recognition work in real-time applications?
Yes, if the API is designed for it. FacialProof is optimized for real-time analysis, making it suitable for live video streams, interactive interfaces, and adaptive user experiences. Emotional and attention signals are processed continuously, allowing applications to respond instantly as expressions change.