eternity report detail
eternity report detail
Face Recognition Using Edge Computing Market

Face Recognition Using Edge Computing Market Size, Share & Growth Analysis By Device Type (Integrated, Standalone), By Component (Introduction, Hardware, Services, Software), By Application (Access Control, Advertising, Attendance Tracking & Monitoring, eLearning, Emotion Recognition, Law Enforcement, Payment, Robotics), And Regional Forecast, 2021-2027

  • No of Pages: 200
  • Published On: Oct 2021
  • Format: PDF
  • Report ID : 40708

Report Summary

Market Synopsis

The global Face recognition using edge computing market has registered US$ 947.5 million in 2020, and projected to reach at US$ 3,218.5 million by 2027, growing at a CAGR of 19.1% during 2021-2027. Face recognition using edge computing is nothing but an extension to the existing Face recognition application which was working on cloud servers. Cloud computing method only involved cloud servers and devices while edge computing involves use of edge devices that may be either IoT or smart devices which processes the data locally I.e., near the source of device which ultimately helps in faster response time, low bandwidth and improved performance.

Requirement of real-time results, reduce error rate and reduce bandwidth to foster the adoption of Face recognition using edge computing market

Edge computing is nothing but the upgrade of cloud of cloud computing. It has an edge which is either a smart or IoT device is located near the source device in order to process the data faster and reduce the processing requirement at the cloud end. Here,  Large number of data sets of individuals are stored in the application for recognizing/identifying the person, the application developed has to process large data sets to provide the output. However , the output provided has a considerable error rate also provide delayed response . Thus, in order to minimize the error rate, the artificial intelligence is been employed in the application. The use of artificial intelligence further increases the storage size and computing power so in order to reduce the error rate and have real time experience edge computing is been adopted. It not only processes the data fast but it can also efficiently leverage the artificial intelligence and 5g wireless technology which in turn, provides real-time results , reduces error rates and reduce the bandwidth.

Going further, software at times can give false alarms and may accuse a innocent person or may not identify the suspected person as the cameras may fail to capture a human face at specific angle . These types of mistakes can create a severe impact on the end-user and may harm the organization reputation.

However, the use of Face recognition software using edge computing does not have any drawbacks expect it is costlier than its cloud counterpart. Apart from this the use of the Face recognition can be misused by using it for spying purpose. If the application is compromised then the there is a huge threat to data privacy of every individual whose records are maintained in that systems . Hence, it is very important that the application must follow secure coding guidelines and may take proper cybersecurity measures to keep the systems safe from hacks.

Device Type Insights

Face Recognition Using Edge Computing

Based on device type, the global market can be segmented into integrated and standalone. The integrated segment is anticipated to be the dominant segment throughout the forecast period. Integrated edge devices perform Face recognition quickly with extreme precision eliminating the delay from large file transmission or from cloud processing. There is no internet required to establish a cloud or network connection for Face recognition process if the data is not stored on the edge device. In that case, only a small encoded format is transmitted to validate with the templates that are hosted on a remote server. This leads to a quick and secure operation completed within milliseconds. Moreover, the lower cost and improved scalability and flexibility is expected to surge the demand for integrated segment among end-users.

Application Insights

By application, the global market has been categorized into Access Control, Advertising, Attendance Tracking & Monitoring, eLearning, Emotion Recognition, Law Enforcement, Payment, and Robotics. The law enforcement segment is expected to be the dominant segment throughout the forecast period. Law enforcement is one of the major applications for Face recognition using edge computing. It is mostly adopted by most developed countries to keep their citizen from several threats such as terrorism or criminal activities.

Regional Insights


North America is the largest market for the Face recognition using edge computing. The major end-user of this application is law enforcement departments. The U.S has the highest market share with North America and despite the privacy issue concerns and some of the false alarms the law enforcement department are the biggest end-user of this application. Several Tech giants have collaborated with U.S states to provide Face recognition applications. Corporate Offices is the second largest industry .The use of Face recognition is used here to secure its premises and restricted areas with these applications.

Competitive Landscape

Some of the prominent companies involved in global Face recognition using edge computing market includes Alphabet, Inc., Apple Inc., Arm Ltd., NXP Semiconductors, MediaTek Inc., Cadence Design Systems, Inc. , IDEMIA, Micron Technology, Inc., Xilinx, Inc., Samsung Electronics, Huawei Technologies Co., Ltd., Microsoft Corporation, Qualcomm Technologies, Inc., Inc, and Intel Corporation.

Report Scope and Segmentation

Face Recognition Using Edge Computing

Frequently Asked Questions

What is the expected CAGR in terms of revenue for global Face recognition using edge computing market during 2021-2027?

The global Face recognition using edge computing market is projected to witness a CAGR of 19.1% during 2021-2027.

What was the value of North America market in 2020?

The market in North America was valued US$ 425.0 Million in 2020

What are the key factors driving the market growth?

Requirement of real-time results , reduce error rate and reduce bandwidth to foster the adoption of Face recognition using edge computing market

What are the top players in global Face recognition using edge computing market?

Some of the leading companies involved in global Face recognition using edge computing market includes Alphabet, Inc., Apple Inc., Arm Ltd., NXP Semiconductors, MediaTek Inc., Cadence Design Systems, Inc. , IDEMIA, Micron Technology, Inc., Xilinx, Inc., Samsung Electronics, Huawei Technologies Co., Ltd., Microsoft Corporation, Qualcomm Technologies, Inc., Inc, and Intel Corporation. 


Table Of Contents

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








3.1. Introduction








3.2. Market Outlook








3.3. Component Outlook








3.4. Device Type Outlook








3.5. Application Outlook








3.6. Geography Outlook








3.7. Competitor Outlook








4. Market Overview








5. Market Insights








5.1. Market Dynamics








5.1.1. Drivers




 Increasing adoption of facial recognition using edge computing




 Growing adoption to resolve latency-specific issues in face recognition applications




 Succoring real-time and intelligent applications








5.1.2. Restraints




 Issues over security and user mobility








5.1.3. Opportunities




 Seamless and personalized experience to improve business processes




 Increasing integration with AI drones and video surveillance








5.1.4. Challenges




 Technical and computational issues with embedded device such as interoperability, accessibility, and configuration








5.2. Cumulative Impact of COVID-19








5.3. Porters Five Forces Analysis








5.3.1. Threat of New Entrants








5.3.2. Threat of Substitutes








5.3.3. Bargaining Power of Customers








5.3.4. Bargaining Power of Suppliers








5.3.5. Industry Rivalry








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








6.1. Introduction








6.2. Integrated








6.3. Standalone








7. Face Recognition using Edge Computing Market, by Component








7.1. Introduction








7.2. Hardware








7.3. Services








7.4. Software








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. eLearning








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








9.6.1. California








9.6.2. Florida








9.6.3. Illinois








9.6.4. New York








9.6.5. Ohio








9.6.6. Pennsylvania








9.6.7. Texas








10. Asia-Pacific Face Recognition using Edge Computing Market








10.1. Introduction








10.2. China








10.3. India








10.4. Indonesia








10.5. Japan








10.6. Malaysia








10.7. Philippines








10.8. South Korea








10.9. Thailand








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








11.1. Introduction








11.2. France








11.3. Germany








11.4. Italy








11.5. Netherlands








11.6. Qatar








11.7. Russia








11.8. Saudi Arabia








11.9. South Africa








11.10. Spain








11.11. United Arab Emirates








11.12. 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








12.3. Market Share Analysis








12.4. Competitive Scenario








12.4.1. Merger & Acquisition








12.4.2. Agreement, Collaboration, & Partnership








12.4.3. New Product Launch & Enhancement








12.4.4. Investment & Funding








12.4.5. Award, Recognition, & Expansion








13. Company Usability Profiles








13.1. Alphabet, Inc.








13.2. Apple, Inc.








13.3. Applied Brain Research








13.4. Arm Holdings








13.5. Cadence Design Systems, Inc.








13.6. Horizon Robotics








13.7. Huawei Technologies Co., Ltd.








13.8. IDEMIA








13.9. Mediatek, Inc.








13.10. Micron Technology








13.11. Microsoft Corporation








13.12. NVIDIA Corporation








13.13. Qualcomm Incorporated








13.14. Samsung Electronics








13.15. Xilinx, Inc.








14. Appendix








14.1. Discussion Guide








14.2. License & Pricing








14.3. Contact Details

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Frequently Asked Questions on this Report

The Face Recognition Using Edge Computing Market is segmented based on Type, Application, and by region.

Key Player 1, Key Player 2, Key Player 3 and Key Player 4 are the leading players of global Face Recognition Using Edge Computing market.

The forecast period would be from 2022 to 2030 in the market report with year 2021 as a base year.

Factors such as competitive strength and market positioning are key areas considered while selecting top companies to be profiled.
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