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Deep learning

We use deep learning models, trained on convolutional neural networks (CNNs), to understand the nuances of human emotions and behaviors.
We detect the 6 basic emotions: anger, disgust, fear, happiness, sadness and surprise.

Computer vision and algorithm

to understand how people feel
Algorithm for emotions facial coding can detect:
170
Landmarks
43
Facial muscles
6
Primary emotions

Why micro expression are so important?

The scientific studies
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FACIAL NERVE
The main function of the facial nerve is motor control of all of the muscles of facial expression. The emotional reaction is 20 - 40 ms which is a time below the threshold of consciousness.
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MUSCLES OF FACIAL EXPRESSION
The facial muscles are 43 and are just below the skin and control facial expression. When they contract, the skin moves forming the micro facial expressions that encode the 6 primary emotions.
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FACIAL ACTION CODING SYSTEM
Paul Ekman's studies have allowed us to codify the 6 primary emotions proving that they are universal, spontaneous and measurable. The Facial Action Coding System is the system that allows the interpretation of the emotions that appear on the face. In addition to micro-expressions that are imperceptible expressions, there are "mini" expressions that often appear in only one region of the face. These small movements can also occur when an emotion has just begun, often before the person is aware of his or her emotional state.
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EMOTION DYADS
Robert Plutchik's studies define that the most complex emotions can be formed by combinations of primary emotions. Dyads define 24 additional emotions that make it easier to understand what people really feel. With our algorithm we can extract this data automatically.

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