Emotion Recognition – How computers see through our emotions
November 12, 2021
Emotion recognition or emotion detection is a method of detecting sentiments based on images, videos, audio, and text leveraging artificial intelligence (AI). In this scenario, emotion recognition technology can use data from different sources like photographs, audio recordings, videos, real-time conversations, and documentation for sentiment analysis.
In recent years, emotion recognition has become increasingly popular. In fact, the global emotion detection market is forecasted to grow to $37.1 billion by 2026.
Part of the “affective computing” family of technologies, the primary objective is to help computers or machines interpret human emotions and affective states by examining non-verbal forms of communication like facial expressions, sentence constructions, the use of language, and more.
Emotion recognition is nothing new. Researchers have been studying it for decades, especially in fields like psychology and human-computer interaction. Today, many companies like Google, NEC, and Eyeris have invested heavily in accelerating the development of facial and emotion detection technology.
What Is Emotion Recognition Training?
For AI to recognize human emotions, it must be trained. You’ll need to train machine learning (ML) algorithms with extensive datasets before successfully detecting and analyzing emotions. You can segment and train ML algorithms based on whether you’re doing emotion recognition in video, audio, text, or conversations.
The more data you have, the better, but it’s crucial to ensure that it adequately represents all races, genders, accents, ages, and so on. This approach is usually dimensional and categorical.
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An emotion detection system incorporated into AI-powered face recognition technology can detect the feelings of a person in any of the following six primary data of emotions categories:
For example, an AI-powered camera with an emotion recognition system can identify a smile on a person’s face as happiness. You can achieve this by training ML algorithms. You can apply the same principles to ascertain the emotional state of a customer during a customer service call.
Sentiment detection occurs when AI determines human emotions in images, text, or speech. At its most basic, sentiment detection concentrates on positive and negative emotions. However, we categorize it further based on how the algorithms are configured and used.
However, this technology is still in its infancy. We have a long way to go before smart algorithms can accurately detect sentiments. To accelerate the process, it’s vital to work with extensive and representative datasets. This is critical if you want to enable cross-cultural emotion recognition.
Why Is Emotion Recognition Important?
Emotion recognition is important because you can use it to enhance education, entertainment, healthcare, marketing, safety, and security initiatives.
For example, during the height of the pandemic, students at True Light College, a secondary school for girls in Kowloon, Hong Kong, attended classes remotely from home. However, unlike most remote learning situations, these students were watched by AI through their computer cameras.
These smart algorithms scrutinized the children’s eyes micromovement of facial muscles. This approach helped teachers make distance learning more engaging, interactive, and personal by responding to each individual student’s emotion and reaction in real-time.
Car companies like BMW, Ford, and Kia Motors are also exploring this technology to assess driver alertness. This can go a long way to keep drivers safe on the roads.
You can also build customer profiles based on available text, audio, and video data. This approach can help you target a specific customer when they are in the best possible emotional state to be more receptive to your offering.
Marketers can also leverage emotion recognition technology to quickly understand if a customer is interested in a product or service and decide on the next appropriate action. In the distant future, emotion detection can also potentially help robots better engage with humans.
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