The Future of Emotion Recognition in Machine Learning and AI

With the recent improvements of machine learning and artificial intelligence, emotion recognition is becoming a new hot topic. It is believed that emotion recognition can improve human interaction with AI. This blog explores the current status and rapidly changing future possibilities of emotion recognition in AI.
The Future of Emotion Recognition in Machine Learning and AI

When you drive your car for a long time, what happens if you fall asleep. It leads to accidents, but what if your steering vibrates and makes a beep sound when falling asleep or feeling drowsy. This is due to the technological advancements in computer vision and artificial intelligence in the automobile industry. This blog talks about the future of emotion recognition in machine learning and AI.

What is emotion AI?

Face expression detection and sentiment analysis from visual input are specific aspects of AI emotion recognition that are active right now in computer vision research. Human-machine interaction is now a vital area of study. AI systems with visual experience try to understand social interaction.

Visual AI emotion recognition

Emotion recognition is the process of machines detecting, interpreting, and classifying human emotion based on facial characteristics.

Visual emotion analysis is a high-level vision task due to the effective gap between small pixels and high-level emotions. Despite the challenges, visual emotion analysis opens up possibilities because comprehending human emotions is a crucial task in achieving robust AI. Due to the fast evolution of convolutional neural networks, deep learning has become the dominant emotion detection and identification model.

Detecting human emotion with wireless signals

Researchers of the Queen Mary University of London said that radio waves were employed to assess heart rate and breathing signals. In addition, predicting how everybody is experiencing even when no other visual indications such as facial expressions are present.

Viewers were first invited to watch a film that researchers had chosen for its capacity to trigger one of four basic emotions: pleasure, joy, sadness, anger.

While the person started watching the film, the researchers transmitted radio signals towards them, similar to radar or Wi-Fi, and analyzed the waves that rebounded back. As a result, the researchers discovered ‘secret’ data for a person’s heart and breathing rates by monitoring changes in these signals produced by simple movements of the body.

The researchers used deep learning models to detect emotions more accurately than machine learning approaches. Deep learning is an approach in which a neural network builds its own properties using a time-dependent dataset.

Market growth of emotion recognition software

Market growth of emotion recognition software

Source: Adroit Market Research.

Adroit market research says the market for emotion detection and recognition (EDR) was worth USD 19.87 million in 2020, and it’s expected to grow to USD 55.86 million by 2028.

Market segments for emotion detection and recognition on a global scale

Application type

  • Law enforcement, surveillance, & monitoring
  • Entertainment & consumer electronics
  • Marketing & advertising
  • Others (e-learning and video games)

End Users

  • commercial
  • Industrial
  • Defense
  • Others (government, retail, entertainment, and transportation)


  • Pattern recognition network
  • machine learning
  • Natural language processing
  • Others (bio-sensors technology)


  • North america
  • U.S.
  • Canada
  • Mexico
  • Europe
  • UK
  • Germany
  • France
  • Rest of europe

Automotive AI for driver monitoring systems

Automotive AI for driver monitoring systems

Source: Affectiva

There are many injuries and fatalities daily caused by distracted driving in the world. 

With the implementation of automated systems and autonomous driving capabilities, car manufacturers face far more safety concerns and considerations. They have to fulfil safety criteria set by industry rules. Like the European new car assessment program (Euro NCAP) and adaptable to changing safety standards.

To increase road safety, automakers must understand the driver’s status, like their emotions, tiredness level and attention, and responses to the drive systems and ride quality. Affectiva Automotive AI uses cameras and microphones to measure complex and subtle emotional and cognitive states using face and voice in real-time. Tailored deep learning frameworks, computer vision, speech recognition, and vast amounts of real-world data power this next-generation in-cabin software. It allows manufacturers to detect and identify driver impairments and develop practical and appealing automobile upgrades to improve safety.


Emotion recognition is significant for machine learning and artificial intelligence. The future innovation in emotion recognition will allow machines to understand how people feel, which is the first step for them to fulfil our needs.

Visionify tailors custom computer vision solutions directly to your specific needs. We help companies solve critical problems and improve the quality of their business. Our solutions gather data from video sources, process it, and understand it, providing valuable results for business processes.