Face Recognition using Edge Computing Market Research Report by Component (Hardware, Services, and Software), Device Type, Application, Region - Cumulative Impact of COVID-19, Russia Ukraine Conflict, High Inflation

Description

List of Tables

The Global Face Recognition using Edge Computing Market size was estimated at USD 1,353.73 million in 2022 and expected to reach USD 1,629.81 million in 2023, projecting growth at a CAGR of 20.53% to reach USD 6,029.95 million by 2030.

Cumulative Impact of COVID-19:

COVID-19 is an unprecedented global public health emergency that affected almost every industry, and the long-term impacts are projected to reflect on the growth of various end-use industries during the forecast period. This ongoing research amplifies the research framework to ensure the inclusion of underlying COVID-19 issues and potential paths forward. The report delivers insights on COVID-19, considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The dedicated section in the report uncovers a detailed analysis of the impact of COVID-19 and its subsequent variant outbreaks on demand, supply, price, and vendor uptake and provides recommendations for sustainable outcomes. The updated study provides insights, analysis, estimations, and forecasts, considering the COVID-19 impact on the Global Face Recognition using Edge Computing Market.

Cumulative Impact of Russia-Ukraine Conflict:

We continuously monitor and update reports considering unceasing political and economic uncertainty due to the Russia-Ukraine Conflict. Negative impacts are globally foreseen, especially across Eastern Europe, European Union, Eastern & Central Asia, and the United States. This contention has severely affected people's lives and livelihoods and represents far-reaching disruptions in trade dynamics. The potential effects of ongoing war and uncertainty in Eastern Europe are expected to have an adverse impact on Ukraine and reverberate harsh long-term effects on Russia. The report uncovers the implications for demand-supply balances, pressure on pricing variants, impact on import/export and trading, and short-term recommendations to the Global Face Recognition using Edge Computing Market considering the current update on the conflict and its global responses.

Cumulative Impact of High Inflation:

The high inflation in developed economies globally has resulted in an overall price surge over the past two years. The cumulatively eroding overall purchasing power is expected to impact developing economies significantly and is considered helpful in numerous ways. The report uncovers the effect of high inflation on the long-term performance of the global economy and provides details on fiscal policies measuring and reducing its short-term impacts on demand/supply, cash flow, and currency exchange. The Global Face Recognition using Edge Computing Market report delivers the high inflation expectation considering the related impact from cost-push and demand-pull inflation.

Market Statistics:

The report provides market sizing and forecasts across 7 major currencies - USD, EUR, JPY, GBP, AUD, CAD, and CHF; multiple currency support helps organization leaders to make better decisions. In this report, the years 2018 and 2021 are considered as historical years, 2022 as the base year, 2023 as the estimated year, and years from 2024 to 2030 are considered as the forecast period.

Market Segmentation & Coverage:

The report on the Global Face Recognition using Edge Computing Market identifies key attributes about the customer to define the potential market and identify different needs across the industry. Understanding the potential customer group's economies and geographies can help gain business acumen for better strategic decision-making. Our market coverage across different industry verticals reveals the hidden truth about the players' strategies in different verticals and helps the organization decide target audience. This report gives you the composite view of sub-markets coupled with comprehensive industry coverage and provides you with the right way of accounting factors such as norms & regulations, culture, to make right coverage strategy for your market plan. This research report categorizes the Global Face Recognition using Edge Computing Market in order to forecast the revenues and analyze the trends in each of the following sub-markets.

Based on Component, the market is studied across Hardware, Services, and Software.

Based on Device Type, the market is studied across Integrated and Standalone.

Based on Application, the market is studied across Access Control, Advertising, Attendance Tracking & Monitoring, E-Learning, Emotion Recognition, Law Enforcement, Payment, and Robotics.

Based on Region, the market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes vendors in the Global Face Recognition using Edge Computing Market. based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinder, N: Niche, and V: Vital). The Global Face Recognition using Edge Computing Market FPNV Positioning Matrix representation/visualization further aids businesses in better decision-making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market, providing the idea of revenue generation into the overall market compared to other vendors in the space. This provides insights on vendors performance in terms of revenue generation and customer base compared to others. The Global Face Recognition using Edge Computing Market Share Analysis offers an idea of the size and competitiveness of the vendors for the base year. The outcome reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Competitive Scenario:

The Competitive Scenario provides an outlook analysis of the various strategies for business growth adopted by the vendors. The news in this section covers valuable insights at various stages while keeping up with the business and engaging stakeholders in the economic debate. The Global Face Recognition using Edge Computing Market Competitive Scenario represents press releases or news of the companies categorized into Merger & Acquisition, Agreement, Collaboration, & Partnership, New Product Launch & Enhancement, Investment & Funding, and Award, Recognition, & Expansion. All the news collected helps vendors understand the gaps in the marketplace and competitor's strengths and weaknesses, providing insights to enhance products and services.

Company Usability Profiles:

The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Face Recognition using Edge Computing Market, including Alphabet, Inc., Arm Holdings, Cisco Systems, Inc., Clarifai, Inc., Huawei Technologies Co., Ltd., IDEMIA Group, IDEMIA NSS, LLC, Innovatrics, International Business Machines Corporation, Micron Technology, Inc., Microsoft Corporation, NEC Corporation by AT&T Inc., NVIDIA Corporation, Oosto by AnyVision Interactive Technologies Ltd., Qualcomm Incorporated, Samsung Electronics Co., Ltd., and Xailient Inc..

The report provides insights on the following pointers:

1. Market Penetration: Provides comprehensive information on the market offered by the key players

2. Market Development: Provides in-depth information about lucrative emerging markets and analyzes penetration across mature segments of the markets

3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments

4. Market Trends: Provides comprehensive understanding of the Cumulative Impact of COVID-19, the Russia-Ukraine Conflict, and the High Inflation

5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players

6. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:

1. What is the market size and forecast of the Global Face Recognition using Edge Computing Market?

2. What are the inhibiting factors and impact of COVID-19 shaping the Global Face Recognition using Edge Computing Market during the forecast period?

3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Face Recognition using Edge Computing Market?

4. What is the competitive strategic window for opportunities in the Global Face Recognition using Edge Computing Market?

5. What are the technology trends and regulatory frameworks in the Global Face Recognition using Edge Computing Market?

6. What is the market share of the leading vendors in the Global Face Recognition using Edge Computing Market?

7. What modes and strategic moves are considered suitable for entering the Global Face Recognition using Edge Computing Market?

Product Code: MRR-031CE94D9C06

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Limitations
1.7. Assumptions
1.8. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Increasing adoption of facial recognition using edge computing
  - 5.1.1.2. Growing adoption to resolve latency-specific issues in face recognition applications
  - 5.1.1.3. Succoring real-time and intelligent applications
- 5.1.2. Restraints
  - 5.1.2.1. Issues over security and user mobility
- 5.1.3. Opportunities
  - 5.1.3.1. Seamless and personalized experience to improve business processes
  - 5.1.3.2. Increasing integration with AI drones and video surveillance
- 5.1.4. Challenges
  - 5.1.4.1. Technical and computational issues with embedded device
5.2. Cumulative Impact of COVID-19
5.3. Cumulative Impact of Russia-Ukraine Conflict
5.4. Cumulative Impact of High Inflation
5.5. Porter's Five Forces Analysis
- 5.5.1. Threat of New Entrants
- 5.5.2. Threat of Substitutes
- 5.5.3. Threat of Substitutes
- 5.5.4. Threat of Substitutes
- 5.5.5. Industry Rivalry
5.6. Value Chain & Critical Path Analysis
5.7. Regulatory Framework
5.8. Client Customization
- 5.8.1. Drones Utilising Machine Learning for Face Recognition

6. Face Recognition using Edge Computing Market, by Component

6.1. Introduction
6.2. Hardware
6.3. Services
6.4. Software

7. Face Recognition using Edge Computing Market, by Device Type

7.1. Introduction
7.2. Integrated
7.3. Standalone

8. Face Recognition using Edge Computing Market, by Application

8.1. Introduction
8.2. Access Control
8.3. Advertising
8.4. Attendance Tracking & Monitoring
8.5. E-Learning
8.6. Emotion Recognition
8.7. Law Enforcement
8.8. Payment
8.9. Robotics

9. Americas Face Recognition using Edge Computing Market

9.1. Introduction
9.2. Argentina
9.3. Brazil
9.4. Canada
9.5. Mexico
9.6. United States

10. Asia-Pacific Face Recognition using Edge Computing Market

10.1. Introduction
10.2. Australia
10.3. China
10.4. India
10.5. Indonesia
10.6. Japan
10.7. Malaysia
10.8. Philippines
10.9. Singapore
10.10. South Korea
10.11. Taiwan
10.12. Thailand
10.13. Vietnam

11. Europe, Middle East & Africa Face Recognition using Edge Computing Market

11.1. Introduction
11.2. Denmark
11.3. Egypt
11.4. Finland
11.5. France
11.6. Germany
11.7. Israel
11.8. Italy
11.9. Netherlands
11.10. Nigeria
11.11. Norway
11.12. Poland
11.13. Qatar
11.14. Russia
11.15. Saudi Arabia
11.16. South Africa
11.17. Spain
11.18. Sweden
11.19. Switzerland
11.20. Turkey
11.21. United Arab Emirates
11.22. United Kingdom

12. Competitive Landscape

12.1. FPNV Positioning Matrix
- 12.1.1. Quadrants
- 12.1.2. Business Strategy
- 12.1.3. Product Satisfaction
12.2. Market Ranking Analysis, By Key Player
12.3. Market Share Analysis, By Key Player
12.4. Product Portfolio Analysis, By Key Player
12.5. Competitive Scenario
- 12.5.1. Merger & Acquisition
- 12.5.2. Agreement, Collaboration, & Partnership
- 12.5.3. New Product Launch & Enhancement
- 12.5.4. Investment & Funding
- 12.5.5. Award, Recognition, & Expansion

13. Company Usability Profiles

13.1. Alphabet, Inc.
13.2. Arm Holdings
13.3. Cisco Systems, Inc.
13.4. Clarifai, Inc.
13.5. Huawei Technologies Co., Ltd.
13.6. IDEMIA Group
13.7. IDEMIA NSS, LLC
13.8. Innovatrics
13.9. International Business Machines Corporation
13.10. Micron Technology, Inc.
13.11. Microsoft Corporation
13.12. NEC Corporation by AT&T Inc.
13.13. NVIDIA Corporation
13.14. Oosto by AnyVision Interactive Technologies Ltd.
13.15. Qualcomm Incorporated
13.16. Samsung Electronics Co., Ltd.
13.17. Xailient Inc.

14. Appendix

14.1. Discussion Guide
14.2. License & Pricing