Many of us have already come across the terms “Emotion Detection or Emotion Recognition” someplace or the other. These terms are have been creating a big buzz in the past and even more so in the COVID-19 era. Many big techs and even startups are involved and engaged with the technology or are marketing and selling products related to the technology.

With the ever-increasing computational powers and much refined deep learning models and the availability of data sets on social media platforms, emotion detection technology is touching newer milestones. Furthermore, the presence of data in the form of videos, audios, images leads to an excellent scope for emotion detection technologies to show their tremendous capabilities.

According to a market research report by Markets and Markets, the Emotion recognition market size is projected to grow from USD 19.5 billion in 2020 to USD 37.1 billion by 2026, at a Compound Annual Growth Rate (CAGR) of 11.3%.

Let’s first look at some of the use cases of Emotion Detection and Recognition technology:

• Market research - Market research companies employ verbal methods such as surveys to understand customer’s wants and needs. The technology comes in handy to market research companies to measure moment-by-moment facial expressions and emotions (facial coding) automatically.
• Safe and personalized cars - Using facial emotion detection, smart cars can alert the driver when he is feeling drowsy. Facial Emotion Detection can capture facial micro-expressions, leading to alerting the driver and suggesting stopping for a coffee break, changing music or temperature.
• More in-depth interviews - Unilever has already started using this technology in its recruitment process. With this technology, a recruiter can assess the overall confidence level of an interviewee that helps make a decision about a candidate’s future performance for client-facing jobs. The technology also finds its application in HR in devising recruiting strategies and in designing HR policies that bring about the best performance from employees.
• Virtual assistants - Various virtual assistants like Siri and Alexa are getting better equipped at their emotion detection capabilities. They can now change the language and tone based on the user’s emotions.
• Video games testing - Video games evoke various emotions in the players, however remembering and expressing the exact feeling and what phase evoked, which reaction is usually difficult to gather and pinpoint. For testing and improvement, this feedback is critical, and hence facial emotion detection can aid in analyzing gamer emotions in real-time as he is playing.

Some more areas where emotion detection is exhibiting tremendous success are:

Law enforcement, surveillance, and monitoring - In countries like China, AI emotion recognition technology has been used in many sectors, including police interrogation and school behaviour monitoring. In the situation of crisis or for surveillance camera videos, the technology can be used to observe and understand people’s reactions and responses.

Marketing, advertising, media, entertainment, and PR - The AI algorithms will try to determine your emotional expression based on several factors such as eyebrows, eyes, and mouth. These models detect 8 emotions: neutral, happy, sad, surprise, fear, disgust, anger, and contempt in faces. 

Businesses and customer bases are made up of people who have their needs and feelings and have expectations. Hence PR agencies take the help of the emotion detection capabilities for aligning the brand value with the face or the leader of that business. As people mostly associate the brand with its CEO or the founder.

The market segmentation as per the End User for emotion detection is industrial, commercial, defence and security agency, enterprises, and others.

However, there are two major cons to this technology: bias and inaccuracy that unknowingly creep in. The bias is based on race, gender, age group, education, or some other demographic factor. With larger data sets, the issue gets magnified even further and might have meaningless or hollow implications.

Another reason for doubt that bothers many is that facial configurations cannot be matched to fingerprints and hence are not sacrosanct when conveying an emotion. It varies from person to person, and also, humans are quite capable of hiding their emotions or suppressing a dominant emotion with another emotion.

Hence the scenario might have serious repercussions where serious decision making is involved or if it affects people’s everyday lives, rights, or opportunities.

This can be deduced easily based on the increasing application of the technology that this technology is here to stay and prove its worth. However, we also know that error rates and problematic biases are quite consequential when it comes to law enforcement, hiring, and psychiatry. But the fact that this technology is amazingly contributing to areas marketing, PR, gaming testing, and voice assistants points towards the need for refining and polishing the algorithms to make them more appropriate. We need to be more vigilant and cautious around eradicating bias and reducing chances of error to make the most out of this technology.