As demand for predictive analysis and automation soars, companies are now looking for solutions that could help them better understand their customer’s sentiments towards specific products and services. To this effect, many companies are now leveraging Emotion AI capabilities to distinguish different sentiments.

According to research, the AI Emotion Recognition market could reach an estimated $43.3 billion by 2025, up from $19.5 billion in 2020[1]. The industry’s tremendous growth comes as no surprise considering the sheer volume of the technology’s applications.

But, despite the multi-billion-dollar industry’s promise to accurately detect emotions from facial expressions, there are a few concerns regarding the technology, ranging from built-in biases to privacy concerns [2].

In this article, we’ll dive into the intricacies of AI emotion Recognition, from what it is, how it works, use cases, and the possible hurdles you might expect if you decide to implement it. Read on for more insight.

What Is AI emotion recognition?

AI emotion recognition, also referred to as affective computing, is a branch of artificial intelligence (AI) that deals with processing and replicating human emotions. At its core, this revolutionary technology aims at making human-machine interactions more natural and authentic.

The technology is based on the universal emotion theory, which claims that all humans, regardless of demographic and nationality, display six internal emotional states using the same facial movements as a result of their evolutionary and biological origins[3]. The basic emotion states include happiness, fear, anger, surprise, disgust, and sadness.

Affective computing can detect people’s feelings through their voice tone, text, gestures, and facial expressions and adjust their demeanor accordingly. Its algorithms achieve this level of human emotion interpretation by employing various technologies and techniques such as speech science, computer vision, and deep learning algorithms.

How AI emotion recognition works?

AI emotion recognition leverages machine learning, deep learning, computer vision, and other technologies to recognize emotions based on object and motion detection. In this case, the machine treats the human face as an object. Through computer vision, the machine can observe facial features like the mouth, eyes, and eyebrows, notice their position, and track their movements over time. It then compares the captured data from the movements with already learned emotions.

Affective computing technologies identify each emotion as an Action Unit (AU) and then link it to a specific emotion. For instance, if the machine observes both the AU ‘upside-down smile’ and the AU ‘wrinkled forehead,’ it can conclude that the person is sad. By mixing these basic classifications, an advanced emotion detector can identify more complex feelings, thus adding to the system’s dependability.

Technologies facilitating AI emotion recognition

There is no universal solution when it comes to effective computing. Developers have to choose the most suitable technology for the task at hand or create a new approach. The technologies are primarily based on either machine learning algorithms or deep learning networks.

Machine learning algorithms analyze data in an emotion-controlled environment, learn its characteristics, then recognize the data in real-world situations. The intricate nature of machine learning algorithms means they rely on human intervention and a lot of structured data to come up with accurate AI emotion analysis.

On the other hand, deep learning models try to mimic the way the human brain works. These systems typically consist of layers of machine learning algorithms, each interpreting data differently to learn from it.

Machine learning algorithms

Support vector machines (SVM)

The SVM algorithm is a form of a linear classification typically applied in image processing and facial recognition technology. Experiments by the University of Cambridge on the technology’s application for emotion recognition show an 88% accuracy in emotion analysis [4].

Despite the promising results, the classification time used in the experiments is considerably higher than other ER methods. The recognition accuracy might also be lower in real-world applications as opposed to emotion-controlled environments.

Bayesian classifiers

Bayesian classifiers are machine-learning algorithms based on the Bayes theorem [5]. Unlike other ML approaches, Bayesian algorithms require less training data and can learn from both structured and unstructured data in an emotion-controlled environment.

Random forest (RF)

Random Forest is an ML algorithm applied for regression, classification, and clusterization. RF models are based on a decision tree predictive model specially tailored for processing large volumes of data. The model handles both categorical and numerical data, thus allowing it to recognize emotions and estimate their intensity. The accuracy of RF models varies between 71% and 96%, depending on the detected features’ complexity.

Source: researchgate.com

Deep learning algorithms

Deep convolutional neural networks (CNNs)

CNNs are some of the most popular deep learning approaches to AI emotion analysis and facial recognition technology. Essentially, CNNs use 3D neural networks to process the red, green, and blue elements in an image simultaneously, thus reducing the number of artificial neurons required to process an image. They come in various types [6], and their accuracy ranges anywhere from 84% to 96%.

Source: mobiquity.com

Reccurrent neural networks (RNNs)

RNNs process sequences of data. Unlike traditional neural networks that process bits of information independent of each other, RNNs add loops to layers of information, thus enabling them to detect transitions between facial expressions and ultimately detect more facial expressions.

Applications of AI emotion recognition

Over the past few years, AI emotion recognition vendors have ventured into most major industries. Now, many major organizations are leveraging these technologies to enhance customer experience and revolutionize their data collection strategies. Here are some of the most common use cases of AI emotion recognition across different departments and industries.

Marketing and advertising

Emotion recognition algorithms can help marketers determine which ads resonate better with their target audience. With this information, they can better determine which features to include in their advertisements to promote engagement and boost conversions.

A good example is Entropik Tech, an emotion recognition AI startup that helps marketers come up with effective emotion-based campaigns popular with their target audience, thus boosting ROI. In August 2019, the Bengaluru-based tech startup released an AI-driven platform that works by adopting AI-powered deep learning technologies to predict the emotion metrics of consumers [7].

Read more about How Artificial Intelligence is transforming influencer marketing industry 

Customer service

Organizations can deploy AI emotion recognition technologies at their call centers to enhance customer service. AI-powered affective computing algorithms can pick the best fitting customer care agent for a specific client, give real-time feedback on the customer’s emotional state, and respond in kind to a frustrated customer.

Chatbots equipped with effective computing technologies can also streamline service flow by considering customers’ emotions. For instance, if the system determines the customer is angry, it can switch to a different escalation flow or direct it to a human customer care agent.

MetLife, a US-based insurance company, deployed Cogito’s emotion AI coaching solution[8] to some of its call centers. The software solution could understand the customers’ emotional state and provide the human agents with real-time tips on conversation and conflict resolution. The result? MetLife witnessed a 17% reduction in call duration and a 6.3% improvement in issue resolution.

Healthcare

Numerous players in the health sector deploy AI emotion recognition technologies to help both patients and doctors. Emotion AI technologies can monitor patients’ emotions during surgical procedures and in examination rooms. Likewise, doctors can pair the technology with voice assistants to detect stress levels in patients and respond accordingly.

Companies dealing with issues pertaining to mental health can also deploy emotion recognition technologies to detect suicidal ideation and alert emergency responders to prevent suicides. For instance, Facebook has deployed an emotion recognition software that monitors users’ posts that shows signs of a user having suicidal ideation. The software also alerts local authorities, thus preventing potential suicides.

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Education

Specially designed emotion recognition software can gauge and adjust to learners’ emotions. For instance, if a learner displays signs of frustration owing to a task being too difficult or too easy, the learning software adjusts the task accordingly, making it either more or less challenging according to the learners’ emotions. Some learning software can also help autistic children recognize other people’s emotions.

Car safety

Several insurers and automotive manufacturers use computer vision technology paired with AI emotion recognition software to assess the emotional state of the driver. If the driver displays signs of extreme emotion or drowsiness, the software notifies the company, which then acts accordingly.

For instance, Ford, AutoEmotive, and Affectiva’s Automotive AI have partnered to create an AI emotion recognition vehicle software designed specifically to identify human emotions such as anger, frustration, or drowsiness. If the software recognizes any of these emotions, it can take charge and stop the vehicle, thus preventing accidents and potential road rage incidents [9].

Barriers facing the implementation of AI emotion recognition

Bias

Like with any facial recognition technology, AI emotion analysis struggles with racial bias. Not so long ago, Nikon cameras identified Asian people as blinking [10], and Google once tagged black faces as gorillas [11]. Similarly, a certain emotion analysis algorithm identified back people as being angrier than their white counterparts.

Additionally, there is a looming concern about bias against the elderly [12]. As people age, it becomes more difficult to distinguish between different emotions, especially for machines. The result is that these technologies may discriminate against certain demographics.

For instance, insurance companies and vehicle manufacturers may use AI-powered facial recognition technology to identify drivers’ fatigue based on particular facial expressions. In this case, older drivers may match the criteria, even though they don’t necessarily exhibit any physical symptoms. As a result, senior citizens may have to pay higher premiums.

Privacy concerns

Emotion recognition technologies literally see and hear everything you do. With this information, devices like virtual assistants and mobile apps are better able to adapt to your mood. Although the sentiment behind this snooping is that the technologies can create more personalized experiences and hold more natural, human-like conversations, users have little control over collected data. Even the sensitive information they might not want to be disclosed to anyone.

Just recently, ImageNet, a large dataset used in facial detection and facial recognition technology training, had to blur the faces on over 1.5 million pictures due to privacy concerns[13]. The company may try remaking their models in an emotion-controlled environment, but in the meantime, it will be quite difficult to teach ML algorithms on faceless data.

Final thoughts on AI emotion analysis

Emotion recognition technologies are vital to building empathetic computer systems and improving human-computer interactions based on the users’ emotions. But, despite the numerous benefits in real-world applications, the technology faces several hurdles in terms of bias and privacy concerns. As ML algorithms get smarter, bias may be a thing of the past, but privacy still remains a major concern.

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References

[1] Marketsandmarkets.com. Emotion Detection
Recognition Market. URl: https://www.marketsandmarkets.com/Market-Reports/emotion-detection-recognition-market-23376176.html. Accessed September 5, 2022
[2] Nature.com. URL: https://www.nature.com/articles/d41586-021-00868-5. Accessed September 5, 2022
[3] Pnas.org. Facial Expression for Emotion are Not Culturally Universal. URL:  https://bit.ly/3BoBamw. Accessed September 5, 2022
[4] Cmu.edu. Facial Expression Recognition Using Support Vector Machines.  https://www.cs.cmu.edu/~pmichel/publications/Michel-FacExpRecSVMPoster.pdf. Accessed September 5, 2022,
[5] Machinelearningmastery.com. Theorem for Machine Learning. URL: https://machinelearningmastery.com/bayes-theorem-for-machine-learning/. Accessed September 5, 2022,
[6] Run.ai, Deep Learning for Computer Vision. URL: https://www.run.ai/guides/deep-learning-for-computer-vision/deep-convolutional-neural-networks. Accessed September 5, 2022
[7] Indiatimes.com, Using Human Emotions in Marketing with the Help of AI. URL:  https://bit.ly/3AT4HTM. Accessed September 5, 2022
[8] Hbs.edu. AI for a Better Human Customer Service Experience. URL: https://digital.hbs.edu/platform-digit/submission/cogito-ai-for-a-better-human-customer-service-experience/. Accessed September 5, 2022
[9] Therobotreport. com. Automotive AI Cars Monitor Emotions. URL: https://www.therobotreport.com/affectiva-automotive-ai-cars-monitor-emotions/. Accessed September 5, 2022
[10] Thesocietypages.org. Nikon Camera Says Asians are Always Blinking. URL:  https://thesocietypages.org/socimages/2009/05/29/nikon-camera-says-asians-are-always-blinking/. Accessed September 5, 2022.
[11] Wsj.com. Google Mistakenly Tags Black People as Gorillas. URL: https://www.wsj.com/articles/BL-DGB-42522.
[12] https://www.frontiersin.org/articles/10.3389/fpsyg.2015.01130/full. Accessed September 5, 2022
[13] Biometriccupdate.com. Imagenet Database Blurs Images Used for Facial recognition Training in Privacy Effort. URL:  https://bit.ly/3euYKov. Accessed September 5, 2022

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