首頁 > 市場調查報告書 > 通訊

人工智能

市場調查報告書

商品編碼

1374874

全球情緒偵測與識別市場 - 2023-2030

Global Emotion Detection And Recognition Market - 2023-2030

出版日期: 2023年11月01日 | 出版商:

DataM Intelligence | 英文 201 Pages | 商品交期: 最快1-2個工作天內

價格

※ 本網頁內容可能與最新版本有所差異。詳細情況請與我們聯繫。

簡介目錄

概述

全球情緒偵測與辨識市場在 2022 年達到 222 億美元，預計到 2030 年將達到 444 億美元，2023-2030 年預測期間年複合成長率為 11.4%。

全球情緒檢測和識別市場的推動是由於健康、行銷、零售和安全等各個領域的需求增加。情緒檢測有多種方法用於分析資料和解釋情緒。電腦視覺演算法主要用於影像處理和情感檢測。

對情緒檢測和識別 (EDR) 的需求主要是由人工智慧和機器學習的不斷成長的使用所推動的。 EDR 模型使用 AI 和 ML 演算法進行訓練，以從一系列資料來源（包括語音、生理訊號和臉部表情）中識別和識別情緒。具有人工智慧和機器學習功能的 EDR 系統比傳統的 EDR 系統更精確。

這是因為人工智慧和機器學習演算法能夠識別人們難以看到的資料中的複雜模式。人工智慧和機器學習技術越先進，人工智慧和機器學習驅動的 EDR 解決方案就越便宜。因此，現在更廣泛的組織和企業可以更容易使用它。

亞太地區是情緒偵測和識別市場成長最快的地區，佔整個市場的近2/5。亞太地區是世界上最大、擴張最快的醫療保健市場之一。醫療保健產業正在使用 EDR 技術來增強病患照護和治療效果。

例如，EDR 系統可用於追蹤和識別患者的情緒，這使醫療專業人員能夠更好地了解患者的需求並提供更個人化的治療。該地區的各種研究人員正在投資開發先進技術，為市場創造機會。

動力學

廣泛應用於情緒偵測和識別的感測器

臉部表情和肢體語言是使用視覺感測器（包括攝影機）記錄的。辨識情緒最關鍵的指標之一是臉部表情，它可以傳達多種感受，包括快樂、悲傷、憤怒和恐懼。眼神交流、姿勢和手勢是肢體語言的例子，可以提供有關情緒狀態的重要細節。

語音和其他發聲由音訊感測器（包括麥克風）錄製。言語是辨識情緒的有用工具，包括恐懼、憤怒、悲傷和享受。笑聲和淚水是發聲的例子，可以提供有關一個人情緒狀況的資訊。

檢測和識別情緒的技術有許多可能的用途。它可用於創造新穎的醫療方法、客製化行銷工作和增強客戶服務。此外，它還可用於提供全新的教學和娛樂體驗。監測心臟電活動的非侵入性測試的一個例子是心電圖 (EKG)。它用於各種心臟疾病的診斷和監測。

行銷和廣告需求不斷成長

情緒檢測和識別市場的技術進步為公司提供了有關消費者情緒的寶貴見解。在娛樂領域，這些技術可以幫助公司了解客戶的偏好。因此，採用這些技術的需求增加。它有助於從消費者那裡獲取資訊，這些資訊隨後用於市場成長。

線上平台對其產品進行市場籃分析。它們決定了消費者的搜尋模式。借助該演算法，公司或品牌可以針對特定領域的特定受眾。透過應用關聯規則，他們將產品與其買家聯繫起來。透過識別消費者的心理狀態，行銷人員瞄準了他們的受眾。

隱私和安全問題

捕捉個人影像的情緒偵測和識別會導致隱私問題。它技術分析個人和敏感資料，例如表情和生理過程。它引發了人們對資料和隱私外洩的安全性的擔憂，因為駭客行為增加了人們對與任何人工智慧工具共享個人資料的擔憂。

使用者接受度和信任也是情緒偵測和辨識系統需求下降的一個主要因素。企業需要對用戶資料更加謹慎。將情緒偵測應用程式整合到其他技術中是一項複雜的任務。因為與其他軟體會有相容性問題。

The global emotion detection and recognition market is driven due to increases in demand for various sectors such as the health, marketing, retail and security sectors. Emotions detection has various methods used to analyze the data and interpret emotions. Computer vision algorithms are majorly used for image processing and emotion detection.

The demand for emotion detection and recognition (EDR) is mostly driven by the growing use of AI and ML. EDR models are trained using AI and ML algorithms to identify and identify emotions from a range of data sources, including speech, physiological signals and facial expressions. EDR systems with AI and ML capabilities are more precise than conventional EDR systems.

It is due to the fact that AI and ML algorithms have the ability to recognize intricate patterns in data that are hard for people to see. The more advanced AI and ML technologies get, the less expensive AI and ML-powered EDR solutions get. It are now more reachable by a wider range of organizations and businesses as a result.

Asia-Pacific is the fastest-growing region in the emotion detection and recognition market covering nearly 2/5th of the total market. One of the largest and most rapidly expanding healthcare marketplaces in the world can be found in Asia-Pacific. The healthcare sector is using EDR technologies to enhance patient care and results.

EDR systems, for instance, may be used to track and identify patients' emotions, which enables medical professionals to better understand their patients' requirements and deliver more individualized treatment. Various researchers in the region are investing in developing advanced technology leading to creating opportunities for the market.

Dynamics

Sensors Widely Used in Emotion Detection and Recognition

Facial expressions and body language are recorded using visual sensors, including cameras. One of the most crucial indicators for identifying emotions is facial expressions, which can convey a variety of feelings including joy, sorrow, rage and fear. Eye contact, posture and gestures are examples of body language that can provide important details about emotional state.

Speech and other vocalizations are recorded by audio sensors, including microphones. Speech is a useful tool for identifying emotions including fear, rage, sadness and enjoyment. Laughter and tears are examples of vocalizations that might provide information about one's emotional condition.

Technologies for detecting and identifying emotions have many possible uses. It may be applied to create novel medical treatments, tailor marketing efforts and enhance customer service. Additionally, it may be utilized to provide brand-new instructional and entertaining experiences. An example of a noninvasive test that monitors the electrical activity of the heart is the Electrocardiogram (EKG). It is employed in the diagnosis and monitoring for various cardiac diseases.

Rising Demand in Marketing and Advertising

The advancement in technology in the emotion detection and recognition market in which it provides valuable insights to companies about consumer emotions. In the entertainment sector, these technologies help companies to understand customer preferences. Due to this, the demand for adoption of these technologies increases. It helps to gain information from consumers which is later used for market growth.

Online platforms use market-basket analysis on their product. In which they determine the search pattern of the consumer. With the help of this algorithm companies or brands target a particular audience in a specific domain. By applying the association rule they relate the product and its buyer. By identifying the consumer's state of mind marketer targets their audience.

Privacy and Security Concerns

Emotion detection and recognition which capture images of individual persons lead to privacy concerns. It technology analyzes personal and sensitive data such as expressions and physiological processes. It raises concerns about the security of breaches of data and privacy, as hacking increases people are worried about sharing their personal data with any AI tools.

User acceptance and trust is also a major factor due to which demand for emotion detection and recognition system decreases. Companies need to be more cautious about user data. Integrating emotion detection applications into other technology is a complex task. As there will be compatibility issues with other software.

Segment Analysis

The global emotion detection and recognition are segmented based on technology, application, end-user and region.

Technological Advancements In Facial Recognition

With the rapid growth of facial recognition, it has been used in many industries. It works on facial segmentation of images which detects the emotions of any individual. There are various AI detection models that detect emotions. The emotions detection system works on many different parameters such as eye activity, motion analysis and skin resistance measurements. The technology has gained immense popularity in developed countries like U.S. and Canada due to the rising adoption of advanced technology thus covering more than 34.9% in the region.

The detection of emotion depends upon the local features of the face. For example, if the emotion detection tools detect the person's emotions such as anger or sadness, these factors can lead to detecting physiological responses such as there might be increased heart rate or any other issues. Emotional intelligence plays a major role in the detection and recognition of the decision-making process for individuals.

Companies have invested heavily in the market leading to a boost the segmental revenue. For instance, in October 2019, Fujitsu Laboratories, Ltd. and Fujitsu Laboratories of America, Inc. has announced the development of artificial intelligence (AI) face expression recognition technology that accurately recognizes small changes in facial expression. The new technique was created in partnership with the School of Computer Science at Carnegie Mellon University.

Geographical Penetration

North America: Leading the Growth of Emotion Detection And Recognition Market

In North America, AI and ML technologies are becoming more and more popular, especially in the EDR market. It is a result of the region's expanding computing power and data availability. North American governments are encouraging the development and implementation of EDR technology increasingly. It is a result of governments realizing how EDR technology may boost the efficacy and efficiency of public services. Because multimodal EDR systems are more accurate and less expensive than traditional EDR solutions, they are growing in popularity in North America.

EDR is being utilized in the healthcare sector to identify and track individuals who suffer from anxiety and depression. Additionally, EDR can assist patients in regulating their stress and provide better pain management. EDR is used in education to determine whether students are struggling and to evaluate student participation. EDR can be employed to the development of customized educational initiatives. EDR is being employed in the workplace to raise productivity and employee happiness. Workplace violence may also be identified and prevented with the use of EDR. Future EDR applications will likely offer up even more ground-breaking and innovative concepts as the technology develops.

Competitive Landscape

The major global players in the market include: Kairos AR, Inc., iMotions A/S, Noldus Information Technology BV, Amazon.com, Inc., Realeyes, IBM Corporation, Google LLC, Emotibot Technologies Limited, NuraLogix Corporation, Entropik Technologies Pvt . Ltd.

COVID-19 Impact Analysis

Globally, the pandemic has had a major negative influence on mental health, leading to higher levels of emotional discomfort, worry and stress. Technologies that detect emotions have been used to track and evaluate mental health issues. The significance of these tools in remotely monitoring and treating mental health concerns has been further highlighted by the pandemic

Systems for identifying and monitoring emotions that heavily rely on facial expressions have faced challenges as a result of the COVID-19 pandemic. Due to this masks are frequently used during social interactions as a precaution against the spread of the illness. So was challenging to recognize faces since the mask was leading to a loss of information.

Russia-Ukraine Conflict Analysis

In both accuracy and conclusions, emotion recognition algorithms often need a lot of different types of training data. The Ukraine conflict might restrict information-gathering efforts and have a consequence on the quantity and variety of data used to train emotion recognition algorithms. The growth of emotion detection technology in the area may be limited if data collection attempts are affected by the war.

The regional market dynamics, particularly the market for emotion detection, may be affected by the war. The demand as well as the growth in emotion detection technology in the affected regions, notably Russia and Ukraine, may be impacted by instability, economic difficulties and political uncertainty related to the conflict. Modifications in market growth and investment may result from this.

AI Analysis

AI-powered emotion detection systems may be trained on a variety of datasets, which enables them to fully understand and identify emotions in a wide range of linguistic, socioeconomic and demographic situations. The flexibility and functionality of emotion detection systems in various market segments and industries are facilitated by their ability to adapt and extend.

AI algorithms are built to quickly digest data, allowing instantaneous emotion recognition and detection. It capacity is especially useful in situations requiring fast input or a reaction based on emotions, such as video analysis, virtual meetings or live customer encounters. Real-time data analysis capabilities of emotion detection systems powered by AI allow for rapid insights and adaptive reactions. It advanced feature of emotion detection and recognition system has increased its demand in the market. Making it a solution for both online and offline modes, considering the situation during the COVID-19 pandemic.

By Technology

Machine Learning
Natural Language Processing
Pattern Recognition Network
Others

By Application

Facial Recognition
Speech and Voice Recognition
Bio-sensing
Others

By End-User

Government
Healthcare
Retail
Entertainment
E-learning
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

For instance on 5 July 2022, Zoom announced 'ZoomIQ' which detect human emotions and facial expression. It technology can be used by salespeople so they can pitch their sales according to the emotions of individuals.
For instance on 7 Jun 2023, kouo launches a platform that is powered by AI which is used for emotions analytics.

Why Purchase the Report?

To visualize the global emotion detection and recognition market segmentation based on technology, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of battery collapsible PV systems market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
End-User mapping available as Excel consisting of key End-Users of all the major players.

The global emotion detection and recognition market report would provide approximately 61 tables, 62 figures and 201 Pages.

Target Audience 2023

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet By Technology
3.2. Snippet By Application
3.3. Snippet By End-User
3.4. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Sensors Widely Used in Emotion Detection and Recognition
  - 4.1.1.2. Rising Demand in Marketing and Advertising
- 4.1.2. Restraints
  - 4.1.2.1. Privacy and User Acceptance in the Emotion Detection and Recognition Market
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers' Strategic Initiatives
6.6. Conclusion

7. By Technology

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 7.1.2. Market Attractiveness Index, By Technology
7.2. Machine Learning*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Natural Language Processing
7.4. Pattern Recognition Network
7.5. Others

8. By Application

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 8.1.2. Market Attractiveness Index, By Application
8.2. Facial Recognition*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Speech and Voice Recognition

9. By End-User

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 9.1.2. Market Attractiveness Index, By End-User
9.2. Government*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Healthcare
9.4. Retail
9.5. Amorphous Silicon

10. By Region

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 10.1.2. Market Attractiveness Index, By Region
10.2. North America
- 10.2.1. Introduction
- 10.2.2. Key Region-Specific Dynamics
- 10.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.2.6.1. U.S.
  - 10.2.6.2. Canada
  - 10.2.6.3. Mexico
10.3. Europe
- 10.3.1. Introduction
- 10.3.2. Key Region-Specific Dynamics
- 10.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.3.6.1. Germany
  - 10.3.6.2. UK
  - 10.3.6.3. France
  - 10.3.6.4. Italy
  - 10.3.6.5. Spain
  - 10.3.6.6. Rest of Europe
10.4. South America
- 10.4.1. Introduction
- 10.4.2. Key Region-Specific Dynamics
- 10.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.4.6.1. Brazil
  - 10.4.6.2. Argentina
  - 10.4.6.3. Rest of South America
10.5. Asia-Pacific
- 10.5.1. Introduction
- 10.5.2. Key Region-Specific Dynamics
- 10.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 10.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 10.5.6.1. China
  - 10.5.6.2. India
  - 10.5.6.3. Japan
  - 10.5.6.4. Australia
  - 10.5.6.5. Rest of Asia-Pacific
10.6. Middle East and Africa
- 10.6.1. Introduction
- 10.6.2. Key Region-Specific Dynamics
- 10.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

11. Competitive Landscape

11.1. Competitive Scenario
11.2. Market Positioning/Share Analysis
11.3. Mergers and Acquisitions Analysis

12. Company Profiles

12.1. Kairos AR,Inc.*
- 12.1.1. Company Overview
- 12.1.2. Product Portfolio and Description
- 12.1.3. Financial Overview
- 12.1.4. Key Developments
12.2. iMotions A/S
12.3. Nodules Information Technology BV
12.4. Amazon.com,Inc
12.5. Realeyes
12.6. IBM Corporation
12.7. Google LLC
12.8. Koppers Inc.
12.9. Noldus Information Technology BV
12.10. Entropik Technologies Pvt. Ltd.

LIST NOT EXHAUSTIVE