The many applications of emotion recognition

The many applications of emotion recognition
Human beings have various emotions, which can now be recognized by machines and computers thanks to advanced algorithms. One developer of such algorithms, Opsis, touted emotion recognition as being able to help a range of industries, from retail to healthcare, achieve their business objectives.
 
According to Stefan Winkler, CEO and Co-Founder of Opsis, his company’s solution is unique in that it offers fine-grained estimations along two dimensions: valence (positive vs. negative emotions) and arousal (energetic vs. passive expressions). This helps the system recognize more emotions than the seven main ones – neutral, sad, happy, surprised, afraid, angry and disgusted – in competing solutions.
 
“Facial expressions and emotions are continuous entities with numerous variations. Consequently, they cannot be limited only to seven specific predefined cases. For example, existing systems can identify a happy and a surprised face. However, classification will not be as accurate for a transitional expression between two basic emotions (like a mixture of happy and surprised, or sad and disgusted). Since existing systems build explicit models for each of the seven specific emotions, they are not able to identify robustly combined expressions,” Winkler said. “We detect face(s) and track 49 facial feature points which is much more accurate compare to our competitors. Our circumplex model handle many more expressions compared to the seven prototypical in the current market offering.”
 
According to Winkler, emotion recognition has applications in different sectors, for example retail and education. “Based on our current customer and partners engagements, one of the most promising use cases is marketing/advertising. Our customers want to know how people respond to ads, products, packaging and store design,” he said. “Education applications measure real-time learner responses to and engagement with educational content; adapt and personalize content; and measure effectiveness of lecturer.”
 
Among other verticals that can benefit from this technology are automotive, healthcare and gaming, Winkler said, adding it can play an important part in security, too. “It can identify people in a crowd, monitor citizens for suspicious behavior by tracking identity, age, gender and current emotional state. It can be used to pre-emptively stop criminals and potential terrorists,” he said.
 
Winkler noted that emotion recognition will only grow and find more acceptance among users. “There have been many reports like MarketsandMarkets which estimate the emotion detection and recognition market to grow from US$6.72 billion in 2016 to $36.07 billion by 2021, at a compound annual growth rate (CAGR) of 39.9 percent from 2016 to 2021. Some recent high-profile acquisitions highlight the enormous potential and growing demand for emotion recognition solutions. With all these high profile acquisition, it shows A.I. is set to grow and such technologies are highly sort after,” he said. “Our customers have been very receptive to this new avenue of recognizing and understanding customers’ emotions. Our partners like the SP/SI have expressed interest to incorporate emotion for better campaign and visualize how customers react to their marketing campaign. OEM/SDK manufactures are interested to incorporate into their surveillance solution for smart nation initiatives roll out. They foresee that emotion recognition have strong potential to embed into IoTs and Smart Nation for surveillance, wearable and end sensing devices.


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