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Recognizing emotions in real-time

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Challenge

Every human has six basic emotions: anger, disgust, fear, happiness, sadness, and surprise. At Sagacify, we applied facial recognition technology to develop a machine learning agent that is able to detect human emotions.

Solutions

People themselves find it sometimes difficult to read emotions, so one can easily imagine how hard it must be for a machine to do it. In order to realize this project, two models were required. Both of them are based on deep convolutional neural networks (CNN): The task of the first model is facial detection, while the second one specializes in emotion detection. Thousands of images were used as dataset to train the first model on recognizing faces, while for the second model, a labeled dataset was created consisting of RGB images and video clips.

Both models were subsequently used to recognize emotions in real-time. The first model identifies and extracts each face in every frame of the moving material. Once collected, the second model scans the facial expression and, suggests the correct emotion expressed on the user’s face.

Results

Possible applications

But what’s in it for companies? It might be a valuable asset in the upcoming years in the fields of:

Marketing:

  • How? Monitor crowd feelings, capture emotions at the moment of purchasing…
  • Added value? Evaluate brand reputation, customer satisfaction, data for market studies,...

Healthcare:

  • How? Monitor emotional state during surgery
  • Added value? Improve medical procedures

Traffic

  • How? Monitor emotional state while driving: tiredness, aggressiveness, concentration, ...
  • Added value? Develop preventive measures by alerting abnormal behavior, reduce risk of accidents, increase response time of emergency services

It can already prove useful to some businesses, however, together with new clients, Sagacify will gain the necessary expertise to develop new applications with this technology. Interested? Contact us and we will be happy to discuss the possibilities.

More information about the process can be found in our thoughts section.

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