Face Recognition using Edge Computing Market by Component, Device Type, Application - Cumulative Impact of COVID-19, Russia Ukraine Conflict, and High Inflation

Description

List of Tables

The Global Face Recognition using Edge Computing Market is forecasted to grow significantly, with a projected USD 1,629.81 million in 2023 at a CAGR of 20.53% and expected to reach a staggering USD 6,029.95 million by 2030.

The Cumulative Impact of COVID-19, the Russia-Ukraine Conflict, and High Inflation is expected to have significant long-term effects on the Global Face Recognition using Edge Computing Market. The ongoing research considers the changes in consumer behavior, supply chain disruptions, and government interventions caused by the pandemic. Similarly, the report considers the ongoing political and economic uncertainty in Eastern Europe caused by the Russia-Ukraine Conflict and its potential implications for demand-supply balances, pressure on pricing variants, and import/export and trading. Additionally, the report addresses the impact of High Inflation on the global economy and details fiscal policies measuring and reducing its effects on demand, supply, cash flow, and currency exchange.

Market Segmentation & Coverage:

This research report categorizes the Global Face Recognition using Edge Computing Market in order to forecast the revenues and analyze trends in each of following sub-markets:

Based on Component, market is studied across Hardware, Services, and Software. The Hardware is projected to witness significant market share during forecast period.

Based on Device Type, market is studied across Integrated and Standalone. The Standalone is projected to witness significant market share during forecast period.

Based on Application, market is studied across Access Control, Advertising, Attendance Tracking & Monitoring, E-Learning, Emotion Recognition, Law Enforcement, Payment, and Robotics. The Law Enforcement is projected to witness significant market share during forecast period.

Based on Region, 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. The Americas commanded largest market share of 38.74% in 2022, followed by Europe, Middle East & Africa.

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 well-informed decisions. In this report, 2018 to 2021 are considered as historical years, 2022 is base year, 2023 is estimated year, and years from 2024 to 2030 are considered as forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix is essential for assessing the Global Face Recognition using Edge Computing Market. It provides a comprehensive evaluation of vendors by examining key metrics within Business Strategy and Product Satisfaction, allowing users to make informed decisions based on their specific needs. This advanced analysis then organizes these vendors into four distinct quadrants, which represent varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital(V).

Market Share Analysis:

The Market Share Analysis offers an insightful look at the current state of vendors in a particular market space. By comparing vendor contributions to overall revenue, customer base, and other key metrics, we can give companies a greater understanding of their performance and what they are up against when competing for market share. The analysis also sheds light on just how competitive any given sector is about accumulation, fragmentation dominance, and amalgamation traits over the base year period studied.

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

4.1. Introduction
4.2. Face Recognition using Edge Computing Market, by Component, 2022 vs 2030
4.3. Face Recognition using Edge Computing Market, by Device Type, 2022 vs 2030
4.4. Face Recognition using Edge Computing Market, by Application, 2022 vs 2030
4.5. Face Recognition using Edge Computing Market, by Region, 2022 vs 2030

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. Market Trends
5.3. Cumulative Impact of COVID-19
5.4. Cumulative Impact of Russia-Ukraine Conflict
5.5. Cumulative Impact of High Inflation
5.6. Porter's Five Forces Analysis
- 5.6.1. Threat of New Entrants
- 5.6.2. Threat of Substitutes
- 5.6.3. Bargaining Power of Customers
- 5.6.4. Bargaining Power of Suppliers
- 5.6.5. Industry Rivalry
5.7. Value Chain & Critical Path Analysis
5.8. Regulatory Framework
5.9. Client Customization
- 5.9.1. 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
12.2. Market Share Analysis, By Key Player
12.3. Competitive Scenario Analysis, By Key Player

13. List of Company Mentioned

14. Appendix

14.1. Discussion Guide
14.2. License & Pricing