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A Spanish research group in computer science has developed a new method capable of recognizing the mood of a person through a video analysis of facial expressions. This method has been embedded in an Software Development Kit (SDK) for developers (web and mobile) in fields such as market research, education, gaming. Commercial agreements with technical assistance, and license agreements are being sought.
Facial expression recognition has been widely applied in the field of psychology, video games, health, learning and man-machine interactions in general, being a very active research area.
The concepts of "mood" and "emotion" are often confused in colloquial language and in their formal definitions. However, there is a consensus that marks at least two major differences between these concepts:
• Moods have a longer duration than emotions.
• Moods are related to emotions because a person who is in a certain mood is prone to experience emotions in his/her facial expression.
Current technologies analyze emotions, but they are not able to analyze moods. This method is based on the machine learning of personalized facial expression models for each subject. Once trained, the method performs a dynamic evaluation of the contribution of subject’s facial micro expressions to a certain mood.
This method could be implemented in different computer-based systems or in mobile devices by means of a proprietary SDK and it performs in real time.
Currently there are tools that can analyze people's emotions, but they are not able to analyze and evaluate one's mood.
The existing tools use procedures that focus on the recognition and processing of snapshots, while this SDK is based on video sequences, which allows to dynamically evaluate the mood in real time.
An important innovation is that the SDK offers a subject’s personalization to minimize errors of different subjects’ micro expressions to express their feelings, for this reason, the method of recognition is precise and customizable.
Existing methods are restricted to the identification of emotions (happiness, sadness, etc.) but do not allow the detection of complex constructs such as mood, the activation of which can at the same time comprise different configurations of emotions, sometimes even opposing ones (for example, anxiety can occur in a sad or in happy person). This method solves the problem, giving way to a much more precise analysis and recognition.
Propietary software (SDK)
This technology can be useful for marketing companies willing to offer product strategies based on customer experience, in the education sector (for on-line courses) and for game development to adjust the games’ playability and engagement.