Emotion Detection and Recognition (EDR) Market to Advance as Researchers Develop a FER Technology that can Accurately Predict Personality Traits and Moral Values in Users

  • Analysis
  • 04-January-2022

Scientists have consistently argued on the possibility of whether reading minds through a person's emotions is achievable or not. Several research teams have developed machine learning and algorithms due to AI (Artificial Intelligence), making the inconceivable possible. However, no hard evidence on the matter has been produced until now.
The situation might soon change as a recent study has demonstrated the use of AI to accomplish Facial Emotion Recognition (FER). The technology can produce features that can help predict personality traits and moral values existing in a specific individual. Because correctly identifying personality characteristics is a task that most humans fail at, this development could significantly impact the Emotion Detection and Recognition (EDR) Market.
The team narrated the steps through which the AI works. First, it tracks an individual's emotions with the help of facial emotion recognition. The system tracks their feelings while watching emotionally triggering short videos of about fifteen minutes. Researchers showcased that the emotional response was predicted as per the user's face. Further, they found that the personality and emotions expected with the help of machine learning, correlation, and regression were essentially correct.
The team undertook a task involving 85 participants to calibrate their novel system. They were asked to watch a video wherein the technology analyzed their facial expressions. Simultaneously, participants also completed four well-validated surveys based on personality characteristics and moral values. The tests used were:

  • The Haidt moral foundations test.

  • DOSPERT (Domain-Specific Risk-Taking Scale).

  • NEO FFI personality inventory.

  • Schwartz personal value system. 

Researchers found that a person's emotional responses can be correctly gauged with the help of AI and the video showing method. The tests had an accuracy of 86% while utilizing gradient-boosted trees.

Further, the team discovered that distant videos could identify different types of characteristics. This means that no single video exists by making precise predictions for all of a person's features. On the other hand, the response received needs to be through videos for accurate predictions. 
At last, the study indeed confirms that emotions, both positive and negative, lie at the center of human ethical values. Thus, the paper proposes a unique and exciting way for measuring people's moral code, attitude, and reactions to different events, helping them understand the kind of people they are. 
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