인공지능과 머신러닝 기술의 발전은 감정 감지 및 인식(EDR) 시장을 주도하고 있으며, 헬스케어, 소매, 자동차 등 다양한 산업에서 감정 식별 시스템의 정확성과 적용을 향상시키고 있습니다. 2024년에는 954억 1,000만 달러로 평가되었고, 예측 기간 동안 382억 6,000만 달러에 달할 것으로 추정됩니다.
맞춤형 사용자 경험에 대한 수요 증가와 인터랙티브 엔터테인먼트, 고객 지원, 정신 건강 모니터링에 대한 감정 분석의 적용이 감정 감지 및 인식 시장을 주도하고 있습니다. 이에 따라 시장은 2026-2032년까지 연평균 12.1%의 성장률을 보일 것으로 예측됩니다.
감정 감지 및 인식 시장 정의/개요
감정 감지 및 인식은 표정, 목소리 톤, 텍스트 분석 등 다양한 양식을 사용하여 인간의 감정을 식별하고 이해하는 기술입니다. 이 부문에서는 강력한 알고리즘과 머신러닝 접근법을 사용하여 사진, 음성, 텍스트의 데이터를 분석하여 시스템이 기쁨, 슬픔, 분노, 놀라움과 같은 감정을 인식할 수 있도록 합니다.
또한, 감정 감지 및 인식은 의료 분야의 정신 건강 모니터링, 고객 서비스 분야의 사용자 경험 향상, 교육 분야의 개인화된 학습, 게임 및 가상현실에서 보다 매력적인 인터랙티브 경험 창출 등 다양한 용도로 활용될 수 있습니다.
고객 경험 향상에 대한 관심이 높아지면서 감정 감지 및 인식 기술의 도입이 가속화되고 있으며, 가트너의 조사에 따르면 2020년에는 10% 이하의 고객 서비스에 AI를 활용한 챗봇을 사용하는 조직이 50%를 보일 것으로 예측했습니다. 이러한 AI 시스템 중 상당수는 감정 감지를 통해 보다 공감적이고 개인화된 답변을 제공함으로써 감정 감지 및 인식 시장을 활성화하고 있습니다.
자동차 부문에서는 운전자의 안전과 경험을 향상시키기 위해 감정 감지 기술을 채택하고 있습니다. 유럽 교통안전위원회의 조사에 따르면, 유럽 교통사고의 약 30%는 운전자의 주의력 저하와 피로로 인해 발생합니다. 이에 따라 감정과 주의력 수준을 감지할 수 있는 첨단 운전자 모니터링 장치에 대한 수요가 증가하고 있으며, 감정 감지 및 인식 시장의 성장을 주도하고 있습니다.
또한, 정신건강에 대한 전 세계의 관심은 헬스케어 분야에서 감정 감지 기술의 사용을 증가시키고 있습니다. 세계보건기구(WHO)에 따르면, 우울증은 2021년까지 전 세계 약 2억 8,000만 명에게 영향을 미칠 것으로 예측됩니다. 정신 건강에 대한 관심의 확산은 조기 진단 및 모니터링을 위한 감정 감지 도구의 개발을 촉진하여 시장 성장에 크게 기여하고 있습니다.
감정 데이터 수집과 분석은 프라이버시에 대한 심각한 우려를 불러일으킵니다. 많은 사람들이 자신의 감정이 추적되고 기록되는 것을 우려하고 있습니다. 유럽의 GDPR(EU 개인정보보호규정)과 캘리포니아의 CCPA와 같은 엄격한 데이터 보호 규정은 기업이 감정 데이터를 수집, 저장, 활용하는 방식을 제한하고 있으며, 이는 시장의 성장과 수용을 방해하고 있습니다.
감정 감지 기술은 다양한 집단의 복잡한 인간 감정을 효과적으로 식별하는 데 있어 지속적인 장애물에 직면해 있습니다. 문화적 차이, 감정 표현 방식의 개인차, 인간 감정의 복잡한 특성으로 인해 오해가 발생합니다. 이러한 정확도의 한계는 기술에 대한 신뢰를 떨어뜨리고 본질적인 용도로의 보급을 방해합니다.
또한, 감정 감지 기술의 사용은 조작과 남용에 대한 윤리적 문제를 야기합니다. 이 기술이 모니터링이나 타겟팅 광고에 사용되거나 본인의 인식이나 동의 없이 사람들의 행동에 영향을 미칠 수 있다는 우려가 있습니다. 이러한 윤리적 문제는 모니터링과 규제 강화로 이어져 특정 산업 시장 확대를 저해할 수 있습니다.
The advancements in artificial intelligence and machine learning technologies are driving the emotion detection and recognition (EDR) market, which improves the accuracy and application of emotion identification systems in a variety of industries such as healthcare, retail, and automotive. According to the analyst from Verified Market Research, the emotion detection and recognition market is estimated to reach a valuation of USD 38.26 Billion over the forecast subjugating around USD 95.41 Billion valued in 2024.
The growing need for customized user experiences and the application of emotion analytics in interactive entertainment, customer support, and mental health monitoring are driving the emotion detection and recognition market. It enables the market to grow at a CAGR of 12.1% from 2026 to 2032.
Emotion Detection and Recognition Market: Definition/ Overview
Emotion detection and recognition is the technological technique of identifying and understanding human emotions using multiple modalities such as facial expressions, vocal tones, and textual analysis. This field uses powerful algorithms and machine learning approaches to analyze data from photos, audio, and text, allowing systems to recognize emotions like happiness, sadness, rage, and surprise.
Furthermore, emotion detection and recognition have a wide range of applications, including monitoring mental health in healthcare, improving user experience in customer service, personalized learning in education, and creating more engaging interactive experiences in gaming and virtual reality.
The rising emphasis on improving the customer experience is accelerating the deployment of emotion detection and recognition technologies. According to Gartner research, by 2025, 50% of organizations will utilize AI-powered chatbots for customer service, up from less than 10% in 2020. Many of these AI systems use emotion detection to give more empathic and individualized answers, boosting the emotion detection and recognition market.
The automotive sector is progressively adopting emotion-detecting technology to improve driver safety and experience. According to research conducted by the European Transport Safety Council, driver attention and fatigue account for almost 30% of all traffic accidents in Europe. This has led to an increase in demand for advanced driver monitoring devices that can detect emotions and alertness levels, driving growth in the emotion detection and recognition market.
Furthermore, the global focus on mental health is increasing the use of emotion detection technologies in healthcare applications. According to the World Health Organization, depression will affect around 280 million people globally by 2021. The prevalence of mental health concerns is driving the development of emotion detection tools for early diagnosis and monitoring, which is considerably contributing to market growth.
The collection and analysis of emotional data raise serious privacy concerns. Many people are concerned with the thought of having their emotions tracked and recorded. Strict data protection regulations, such as GDPR in Europe and the CCPA in California, restrict how corporations can gather, keep, and use emotional data, thus impeding market growth and acceptance.
Emotion detection technology faces ongoing hurdles in effectively identifying complex human emotions across varied populations. Misinterpretations occur due to cultural variances, individual variability in emotional presentation, and the complicated nature of human emotions. These accuracy limitations undermine trust in the technology and prevent widespread adoption in essential applications.
Furthermore, the use of emotion-detecting technologies raises ethical problems around manipulation and abuse. There are concerns that the technology may be used for surveillance, targeted advertising, or to influence people's behavior without their knowledge or agreement. These ethical quandaries lead to increasing scrutiny and regulation, potentially impeding market expansion in particular industries.
According to VMR analysis, the software segment is estimated to hold the largest market share during the forecast period. Emotion detection software is highly scalable, allowing organizations to quickly incorporate it into their existing systems and increase usage across numerous platforms. Its adaptability allows for customization in a wide range of applications, including customer service and healthcare. This versatility makes software solutions more appealing to a diverse range of businesses, resulting in widespread adoption and commercial supremacy.
Emotion detection software uses AI and machine learning algorithms to continuously enhance accuracy and performance through data analysis. This self-improvement nature ensures that software solutions keep up with the most recent breakthroughs in emotion identification technologies, ensuring their relevance and efficacy over time. This element makes a substantial contribution to the software segment's market dominance.
Furthermore, software for emotion detection and recognition is frequently less expensive to implement and maintain than hardware-based solutions. Cloud-based software solutions, in particular, eliminate the requirement for a large on-premises infrastructure. This cost-effectiveness makes software solutions more accessible to organizations of all sizes, resulting in increased adoption and market share.
The marketing and advertising segment is estimated to dominate the emotion detection and recognition market during the forecast period. Emotion detection technology enables marketers to measure real-time emotional reactions to commercials, allowing them to build more engaging and effective campaigns. This feature enables organizations to adapt their messaging to trigger specific emotions, resulting in increased brand memory and client loyalty. The possibility for greater ROI encourages widespread adoption in this area.
Marketers can tailor information and recommendations to specific consumers by evaluating their emotional responses. This level of customization boosts user experience, engagement, and conversion rates. The ability to produce emotionally engaging material at scale drives adoption in marketing and advertising applications.
Furthermore, emotion detection gives marketers vital information about consumer behavior and preferences. This data enables more educated decisions about campaign strategy, product development, and customer service enhancements. The ability to optimize marketing efforts across many channels using data promotes this segment's market domination.
According to VMR analyst, North America is estimated to dominate the emotion detection and recognition market during the forecast period. North America is the leader in AI and machine learning adoption, which is critical for emotion detection and identification systems. According to a National Science Foundation estimate, US federal funding for AI research and development surged by more than 40% between 2015 and 2020, reaching almost $1.9 billion in 2020. This investment is fueling innovation in emotion recognition technology.
The North American healthcare industry is increasingly using emotion detection for mental health assessments and patient care. According to the National Institute of Mental Health, an estimated 52.9 million adults in the United States suffered from a mental illness in 2020, accounting for 21% of all adults. This large population necessitates better emotion detection systems in healthcare settings.
Furthermore, the automotive sector in North America is using emotion detection technologies to improve driver safety. According to the National Highway Traffic Safety Administration (NHTSA), motor vehicle traffic crashes killed an estimated 38,824 people in the United States in 2020. This frightening trend is prompting automakers to develop advanced driver monitoring systems that include emotion detection to improve road safety.
The Asia Pacific region is estimated to exhibit the highest growth during the forecast period. The Asia Pacific region is experiencing an increase in the usage of AI and machine learning technologies, which are critical for emotion detection and identification systems. According to an IDC estimate, AI spending in the Asia-Pacific region (excluding Japan) is predicted to reach USD 32 Billion by 2025, with a compound annual growth rate (CAGR) of 30.8% between 2020 and 2025. Increased investment in AI technology is expected to fuel growth in the emotion detection and recognition market.
Furthermore, the extensive use of smartphones in the Asia-Pacific region creates an important platform for emotion detection and recognition technology. According to GSMA's Mobile Economy Asia Pacific 2022 report, the area is predicted to have 3.1 billion smartphone connections by 2025, accounting for 83% of the total connections. This large user base creates ample opportunities for emotion detection applications in mobile devices.
The competitive landscape of the emotion detection and recognition market is distinguished by rapid technology breakthroughs and a wide range of applications across various sectors. Companies are boosting their investments in artificial intelligence and machine learning to improve the accuracy and efficiency of emotion recognition systems.
Some of the prominent players operating in the emotion detection and recognition market include:
Affectiva
com, Inc.
Apple, Inc.
Cognitec
Eyeris
IBM Corporation
iMotions
Kairos AR, Inc.
NEC Corporation
Noldus Information Technology BV
NVISO
Realeyes
Sentiance
Sightcorp
SkyBiometry
Tobii
Q3 Technologies, Inc.
Paravision, Inc.
Emotion Detection And Recognition Market Latest Developments
In February 2022, NEC strengthened its strategic relationship with SAP to accelerate NEC's corporate transformation (CX) and co-create business opportunities. It will use the latest SAP technologies to accelerate CX, based on the results of the SAP reforms it has implemented solutions.
In February 2022, IBM purchased Neudesic, a renowned US cloud services firm focused mostly on the Microsoft Azure platform and offering expertise in multi-cloud. This acquisition expands IBM's offering of hybrid multi-cloud services and advances the company's hybrid cloud and AI strategy.