Facial Coding

A Natural, Frictionless Way to Measure Human Response

We live in the age of distraction and attention is the gatekeeper to an ad’s success. Without audience attention, everything else is secondary.

“By 2024, AI identification of emotions will influence ​more than half of the online advertisements you see.” ​ ​

Complete Tracking

Face detection • Facial Action Units • 3D head pose • Attention: Capture, Retain & Encode • 7 Basic emotions • 3 Proprietary Emotions • 150 Viewers per video

Fully Optimized

Non-exaggerated natural reactions • Complete in-the-wild environment • Video watching in mobile and desktop web browsers • Precision over speed

Reported Data

Performance: Quality Score • Attention Metrics: Capture, Retain and Encode • Emotional Key Moments • Emotions by Duration • Emotions by Video Segments • Emotions by Demographic

Triple Refinement

Both people and machines recognise emotions in the same way; separating background with the foreground, the ability to focus – and detect the face and shape of its expression.

Face Detection

Not to be confused with facial recognition – we developed our own face detector which is more accurate and reliable than the Viola-Jones detector, generally considered the industry standard.

Feature Detection

Once the region of the face has been detected, we need to be able to identify the landmark points of the face: the nose, the eyes, the mouth and the eyebrows.

Expression Level

Using all the frames in respondent’s video, we create a person-dependent baseline, or mean face shape. By measuring their expressions as they deviate from their ‘neutral’ face, accounts for people who naturally look more positive or negative and so correcting any bias.

Tech That’s at Home in the Wild

Bad lighting, heavy shadows, thick facial hair, glasses, typically makes emotion detection more challenging. However, we use ‘in the wild’ datasets that employs machine learning that teach our algorithms to cope with such obstacles. Get more information on how we collect respondent data from our tech white paper.
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Human Level of Accuracy

To review the accuracy of our motions, we plot the aggregate output of our metrics against ground truth data – the majority voting of 5 human annotators who notate each frame.

Through machine learning and testing tens of thousands of videos, each with a minimum of 150 viewers per video, accuracy of attention and emotional measurement improves continuously, interpreting responses just like a human.

Unleash the Power of Emotion AI

We help brands, publishers and agencies to move fast and efficiently, to maximize their ROI on ad attentiveness.