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Facial Coding

Databrain LAB

Facial Coding

Studying emotion through facial expression

What is it?

Facial coding analyzes in real time the facial movements that reveal a person's emotional state. Using computer vision algorithms, we capture subtle reactions — joy, surprise, frustration or disinterest — during exposure to ads, products or digital experiences.

Facial action unit analysis in the Databrain software

Scientific basis: the analysis rests on facial action units (FACS) — the micro muscular movements that make up each emotion.

An essential tool for neuromarketing

At Databrain we use automated facial expression analysis to understand how consumers respond emotionally before forming a conscious opinion.

This approach makes it possible to
  • Identify the moments of greatest emotional impact in ads and content.
  • Detect negative or rejection responses that would otherwise go unnoticed.
  • Measure the intensity and emotional valence — positive or negative — of each stimulus.
  • Validate advertising narratives, UX experiences and product tests.

Detecting the 7 basic emotions

Our advanced facial recognition technology identifies in real time the seven universal emotions described by scientific research. Through the analysis of muscular micro-contractions, it detects subtle changes in facial expression that reveal the consumer’s emotional state, even before they are aware of it.

Joy Anger Fear Surprise Sadness Contempt Disgust
This data lets researchers and brands
  • Assess the emotional response to ads and creative assets.
  • Measure spontaneous reactions to products, packaging or sensory experiences.
  • Analyze emotional impact during digital or user interactions (UX).
  • Understand why a message connects, persuades or provokes rejection.

Valence and emotional engagement analysis

At Databrain, facial coding measures two variables that are essential to understanding consumer emotional response: valence and engagement.

Emotional valence

It represents the overall tone of the emotion, on a scale from negative to positive. High valence points to pleasant emotions — satisfaction or joy — while low valence reflects negative reactions such as displeasure or frustration. It is key to assessing brand perception, user experience and preference for a product or message.

Facial engagement

It measures how expressive and emotionally involved someone is with a stimulus — an ad, a video or an interactive experience. High engagement reflects an intense, committed reaction; low levels point to emotional disconnection or disinterest.

By combining both metrics, Databrain delivers a precise reading of how — and how intensely — people connect emotionally with brand stimuli, helping optimize communication, design and customer experience strategies.

Analysis capabilities

Beyond emotions: how the engine processes the face to turn every gesture into reliable data.

Action unit detection

The engine identifies in real time the facial muscle movements (the Action Units of the FACS system) that make up each expression.

Head orientation

Records head rotation —yaw, pitch y roll— to contextualize and validate the expression reading.

Interocular distance

Estimates the participant's distance from the camera to keep measurements consistent throughout the study.

Pre-recorded video analysis

Import external recordings and process expressions offline, with no live capture needed.

Built-in quality control

Tools that validate recording quality to discard noisy data, before and during the study.

Flexible data export

Export expression metrics, action units and time series in an open format, ready for custom analysis.

Frequently asked questions

What is facial expression analysis (facial coding)?
It is a method for quantifying emotional expression. It measures the impact of any content, product or service designed to provoke an emotional response, based on spontaneous facial reactions.
What is the Facial Action Coding System (FACS)?
It is an anatomy-based system that objectively measures every visible facial muscle movement. Instead of labeling something “an angry face”, FACS identifies the specific action units (AUs) that make up the expression: brows lowering, lips tightening, and so on.
How do expressions translate into emotions?
The algorithm automatically detects facial action units and, through machine learning, interprets their combinations. Those combinations map to basic emotions — joy, anger, surprise, disgust — following the research of Ekman and Friesen, who linked AU combinations to universal emotional expressions.
Is it invasive or uncomfortable for participants?
No. It only needs a camera pointed at the face; nothing is attached to the person, who can behave naturally. That makes it possible to record emotion in close-to-real conditions.
How does facial coding differ from EEG or GSR?
Facial coding reads emotion from facial expression; EEG measures the brain's electrical activity, and GSR measures physiological arousal through the skin. They are complementary: together they give a complete reading of attention and emotion.
Databrain

What the face reveals before words do.

We offer facial coding as a detection technology and a tool for deep emotional interpretation. We turn micro-expressions into actionable insights that explain why an experience connects. Book a demo
Research

Publicaciones científicas con Facial Coding

Poker face and steady voice: Gender and reactions to emotional neutrality in crowdfunding Jennie Jiongni Mao, Charles Williams, Silvia Stroe, Charlotta Sirén Journal of Business Venturing Insights, Volume 25 (2026) View publication →
Databrain LAB

Other methodologies

Screen-based Eye Tracking

Visual behavior analysis on screen.

Eye-tracking Glasses

Visual attention in real settings.

EEG

Electrical activity of the brain.

GSR

Electrodermal activity and emotional arousal.

ECG

Cardiac response and arousal.

Contact

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