Chicago, Sept. 27, 2023 (GLOBE NEWSWIRE) -- The global Emotion Detection and Recognition Market size is projected to grow from USD 23.5 billion in 2022 to USD 42.9 billion by 2027, at a Compound Annual Growth Rate (CAGR) of 12.8% during the forecast period, according to a new report by MarketsandMarkets™. The major factors driving the market growth include the adoption of AI, ML, and deep learning technologies worldwide, rising need for emotion recognition models by intelligent systems, and growing popularity of wearable devices.
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Emotion Detection and Recognition Market Dynamics:
Drivers:
- Adoption of AI, ML, and deep learning technologies worldwide
- Rising need for emotion recognition models by intelligent systems
- Growing popularity of wearable devices
Restraints:
- Insufficient database and technical problems to lead to discrepancies and false results
- High production cost
Opportunities:
- Increasing demand for IoT technology and applications
- Increasing government initiatives to leverage benefits of EDR technology
List of Key Players in Emotion Detection and Recognition Market:
- NEC (Japan)
- IBM (US)
- Microsoft (US)
- Apple (US)
- Google (US)
- Tobii (Sweden)
- Affectiva (US)
- Elliptic Labs (Norway)
- Intel (US)
- Cognitec (Germany)
Trend: Facial Emotion Recognition Using Machine Learning
Facial recognition has existed for a long time and with advancements in technology, human mood may be roughly predicted from facial expressions and brain activity that have been recorded in video, electric signal (EEG), or picture form. Human emotion recognition is the need of the hour so that cutting-edge artificial intelligence systems can mimic and predict facial expressions. Making educated judgments about the determination of purpose, the advertising of offerings, or security-related dangers might benefit from this. While recognizing emotions from photos or video is a simple operation for the human eye, it is extremely difficult for computers to do and necessitates the use of several image processing algorithms for feature extraction. For this task, a variety of machine learning techniques are appropriate. Machine learning algorithms must first be trained before being tested against an appropriate dataset for any detection or recognition.
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Trend: Facial emotion recognition in content moderation
Facial recognition technology is useful for social media because it allows websites to swiftly identify violent or improper photographs that are posted on their pages. One of the biggest social media platforms, Tumblr, for instance, has prohibited several types of improper content from its website, including pornographic images, videos, and graphics. Face recognition guarantees that future occurrences of this kind are less likely. The program can assess the visual content of images and guess the age and gender of the participants owing to image recognition skills.
Another facial recognition feature for social media was launched by Facebook a few years ago. The face recognition software gives users the option to "tag" all of the individuals in their digital photo albums with a single click, linking their accounts to the images. Facebook built one of the largest repositories of digital photos in the world (although this system was later shut down due to public scrutiny).
By vertical, the Media and Entertainment segment accounts for the highest CAGR
Media and Entertainment vertical accounts for the highest CAGR during the forecast period. Online media streaming platforms such as Netflix, Disney Hotstar, and Amazon Prime have gained popularity in recent years, leading in the rise of the media and entertainment sector. Apart from that, the business is leaning toward using real-time monitoring of the audience's emotional responses and delivering customized content to them. As a result, the media and entertainment segment are expected to have the greatest CAGR throughout the anticipated period.
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By end user, defense and security segment to account for a larger market size
The defense and security agency sector are expected to have the largest market share over the forecast period due to high demand in security, data privacy, and portability. Moreover, Al-based technologies like ML and NLP are employed to boost a nation's defense and security by driving the emotion detection and security agency end-user sector. Furthermore, organizations are focused on delivering innovative solutions to support core response efforts, ensuring the safe flow of goods and resources, and maintaining consistent communication. As a result, it is predicted that the defense and security agency segment will have the largest market during the forecast period.
Opportunity: Increasing demand for IoT technology and applications
The Internet of Things (IoT) technology makes it simpler to monitor and respond to all devices depending on a person's emotions, mood, and actions. Certain equipment, such as sensor input that affects the output of an industrial process at a remote location, communicate with one another without the requirement for human contact. The usage of loT applications is now increasing due to digital processes, productivity, cost savings, dependable data collecting and analysis, and other aspects. EDR technology may therefore be linked with loT applications to collect physiological, behavioral, and response data and responses for analysis and automated replies. As a consequence, better decisions will be made, making processes more efficient. According to DataProt's 2022 projection, there will be 25.4 billion active loT devices by 2030, connecting 152,200 times per minute by 2025. Therefore, by 2025, the potential economic value of loT solutions would reach USD 4-11 trillion. Moreover, IoT technology has enhanced corporate efficiency by 83%, and global loT investment will equal USD 15 trillion between 2019 and 2025. These results suggest that loT applications are increasing across the board, and the market for EDR will rise as a result of the rising number of loT applications. possesses a context menu.
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