If you take a look at any picture of a person you see online, you can most definitely tell what they are feeling or what their mood is. This is something we do subconsciously, as we know the feelings and know what the mood swings are. Yet, what about technology?

Let’s say we have a sophisticated AI-powered machine. Our goal is to train this machine to recognize human emotions. You might be wondering if that’s even possible at all. But the truth is that such systems already exist, and they have already made great progress. In artificial intelligence, the process is called emotion recognition. 

A good example of this technology, and a precursor of emotional AI at the same time, would be a face recognition system. It is often integrated into a device’s camera used to analyze human faces. Case in point, the Face ID feature in iPhone smartphones. Therefore, emotion recognition is one of the numerous face recognition solutions that have evolved and expanded throughout time. Let’s find out what lies behind such an intriguing invention in the field of AI, now capable of emotions!

AI Emotion Recognition: An Asset or a Liability? 

Gartner has forecasted that 10% of personal devices will be equipped with emotion AI features by 2022, either on-device or through cloud services.

The goal of emotion-decoding technology is to train advanced computer systems to anticipate human behavior. Hundreds of companies worldwide are working on this sophisticated technology. For example, digital behemoths like Amazon, Microsoft, and Google, all provide fundamental emotion analysis. Meanwhile, smaller businesses specialize in this tech solution for certain industries, including the automotive, advertising, and recruiting sectors.

So, what is emotion recognition in AI? We are not only logical thinkers, but also emotional beings by nature. And so human behavior can be significantly affected by emotions. The latter is something we are able to comprehend throughout an interaction with another human, which helps us adjust to their emotional needs or requirements. 

The following underlying tenets are shared by all emotion recognition systems in AI: 

There is a limited number of clearly defined and universal emotional categories that humans unintentionally display on their faces, and which machines can be taught to recognize.

With that said, emotion recognition in AI refers to the process of creating and training advanced systems that can decipher what emotional spectrum human beings experience. If that’s not enough for you to understand what emotion recognition is all about, Label Your Data has much more on this topic. 

Emotion recognition technology makes use of AI to identify and classify emotions into the seven universal categories, which are anger, disgust, fear, surprise, happiness, sorrow, and contempt, or a mix of a few. That is, the technology analyzes the facial expressions it observes in the targeted individual. This method, however, raises some serious issues. Let’s talk about them. 

Is Emotion Recognition a Safe Solution for Humans?

A major objection to the adoption of emotion recognition technologies lies in the fact that our emotional state is not always correctly reflected by what a machine can see on our faces. Judging by facial expressions alone might be inaccurate and often misleading. 

The human gamut of emotions is closely intertwined with our body language, tone of voice, changes in skin tone, personality, as well as the environment in which these emotions are perceived. Thus, only in general symbiosis do these aspects create a full-fledged mechanism we use to communicate our emotions, and are all key aspects in conveying how someone is feeling. 

And here’s the trouble. AI-based systems are unable to recognize all these features, making emotion recognition quite inaccurate. What should also be kept in mind is that while emotion recognition technology may potentially be developed to be completely accurate, face recognition technology cannot, even though they are used in conjunction with one another. 

Other issues posed by emotion recognition technology with AI include:

Bias

One area where bias in emotional AI may have a tangible impact is recruitment. That is, AI algorithms monitor job candidates’ facial expressions and make determinations about their employability. The system evaluates the dependability, conscientiousness, emotional intelligence, and cognitive capacity of a potential recruit. The problem is that all these decisions are made based on face recognition, which again might provide inaccurate and biased data. 

Confidentiality

Many analysts wonder whether AI should ever predict how people would behave, especially without our consent. This remains a moot point, even if facial expression algorithms become extremely accurate. The EU’s suggested AI legislation addresses the issue of a person’s right to privacy about their sentiments. The proposal classifies emotion recognition technologies as “high-risk.” It also demands that the people on which the technology is used should provide their explicit agreement.

Key Industry Applications of Emotion Recognition Technology

Even though there are some serious concerns about the bias and accuracy of emotion recognition with AI, many researchers remain optimistic about the technology. They believe emotional AI will advance as companies start to develop solutions that are particularly tailored to national regulations and when training data becomes more appropriate for industrial applications.

The need for technologies capable of determining the demands of potential consumers and selecting the best course of action for them is growing drastically. This is due to the swift uptake of smart technology in society and the growth of global industries. Hence, technology that recognizes human emotions is a crucial asset for domains like:

• Education: Enhanced knowledge transmission, perceptual techniques, and learning processes.
• Healthcare: Detection of disorders like dementia and depression and communication with elderly patients through nurse bots.
• Media & Entertainment: The proper forms of entertainment for the target market.
• Marketing: Personalized ads depending on the emotional condition of a prospective consumer.
• Robotics: Intelligent collaborative or service robots that can communicate with people.
• Retail: Gathering data on visitors’ moods and responses, as well as their demographics.
• Banking: Analysis of advertisement reaction using face recognition.
• Insurance: Fraud detection using voice analysis. 
• Video gaming: Adapting to a player’s emotions using computer vision tech. 
• Employee safety: Monitoring the level of stress and anxiety among employees.
• Public service: Emotion recognition through surveillance cameras. 
• Self-driving cars: Analysis of the driving experience through cameras and sensors. 

As you can see, emotion recognition technology is a demanding solution in the modern AI world. Moreover, it enhances the development of already existing solutions and tasks like computer vision. Yet, each of these industrial applications of emotional AI requires a well-annotated set of data. Discover more here to explore the value of labeled data for emotion recognition with AI and other projects.

Here are some real-world scenarios of adopting emotion recognition technology as well. For instance, the technology has been used by Disney to gauge volunteer responses to several of its films, including Star Wars. Plus, emotional AI is intended to be used by car manufacturers including Ford, BMW, and Kia Motors to analyze driver attention. It has also been put to the test by marketing companies like Millward Brown to see how consumers react to commercials for customers (e.g., Coca-Cola and Intel).

Summary: Emotional AI Is Not Emotional Enough?

The very idea of artificial intelligence capable of emotions seems odd. However, technology evolves fast and is already impacting many areas of our personal lives, work, and society overall despite all the controversies it raises. 

Nevertheless, the ultimate objective of emotion recognition technology is making humans less enigmatic and incomprehensible while more predictable on a large scale, regardless of the industry application. Still, it’s important to constantly be mindful of privacy laws and inherent bias. 

Researchers argue that while AI emotion recognition is increasingly used to read people’s facial expressions, it doesn’t always mean it can infer what those people are truly feeling, thinking, or what they intend to do next. Would you trust AI to read your emotions?