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全球自动指纹辨识系统市场 - 2023-2030

Global Automated Fingerprint Identification Systems Market - 2023-2030

出版日期: 2023年09月27日 | 出版商:

DataM Intelligence | 英文 202 Pages | 商品交期: 最快1-2个工作天内

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简介目录

概述

全球自动指纹辨识系统市场在 2022 年达到 85 亿美元，预计到 2030 年将达到 678 亿美元，2023-2030 年预测期间复合年增长率为 23.2%。

日益增长的安全威胁以及对可靠识别和身份验证方法的需求推动了执法、边境控制和访问控制等各个领域采用自动指纹识别系统。指纹辨识技术的进步，包括准确和更快的指纹匹配演算法，使得自动指纹辨识系统更有效率和可靠。

指纹辨识是一种被接受的生物辨识认证方法，其在行动装置、金融交易和身份验证等应用中的采用正在不断增长。全球许多政府已为国家身分证计划、护照控制和犯罪识别资料库实施了自动指纹识别系统，促进了自动指纹识别系统市场的成长。

到2022年，亚太地区预计将成为全球自动指纹辨识系统市场成长最快的地区，约占市场的1/4。该地区的政府和组织越来越多地将生物识别解决方案用于各种目的，包括执法、边境管制、国家身分识别计划和存取控制。

动力学

不断发展的政府流程

自动指纹辨识系统提供高水准的安全性，并透过指纹准确验证个人身份，这项进步对于国家安全、边境管制和执法至关重要。它透过资料库识别犯罪现场发现的指纹，帮助法律机构解决犯罪问题，这也有助于识别具有多重身分的罪犯。

例如，2023 年 8 月 12 日，印度国家犯罪记录局 (NCRB) 的国家自动指纹识别系统 (NAFIS) 团队荣获印度商务部颁发的数位化转型政府流程再造卓越一类金奖。行政改革与公众申诉（DARPG ）。

联邦内政部长兼合作部长 Shri Amit Shah 祝贺 NAFIS 团队的这项成就。 Shri Amit Shah 讚扬了 NAFIS 团队致力于创建万无一失的指纹识别系统，这符合印度总理莫迪 (Shri Narendra Modi) 的安全愿景。

人们对支付安全的担忧日益加深

自动指纹辨识系统透过启用指纹辨识等生物辨识方法来增强支付安全性。消费者将他们的支付帐户与指纹关联起来，这使得未经授权的用户很难存取他们的财务资讯。使用者透过线上支付的两因素身份验证流程进行身份验证。

例如，2023年9月5日，华为行动服务与阿联酋多家领先银行建立策略合作伙伴关係，以提升该地区的数位银行格局。这些合作包括 ADCB、ENBD、FAB、Mashreq、ADIB 和阿联酋渣打银行等银行，这些合作伙伴关係旨在为华为用户提供更广泛的金融服务，使他们能够透过银行应用程式存取自己的帐户并进行支付。华为应用市场。

技术进步

生物辨识技术的不断发展，包括指纹识别，在市场的成长中发挥了重要作用。它透过指纹准确地验证个人身份，提供高水准的安全性，这为增强私人和公共部门的安全性提供了宝贵的工具。此外，它还可以实现快速、准确的指纹匹配。

例如，2023 年 6 月 6 日，装置智慧平台 Fingerprint 推出了 Fingerprint Pro Plus，其中引入了智慧讯号，这是一项旨在加强诈欺预防工作的创新。 Smart Signals 提供基于 Fingerprint 浏览器和装置识别讯号的即时、可操作的智能，目前有 6,000 多家公司使用该讯号来预防诈骗。

该技术使该公司使用的平台和决策引擎能够快速适应浏览器和行动应用程式技术的变化，从而提高识别诈欺活动的准确性。

实施和维护是一个复杂的过程

准确性取决于指纹影像的品质和所使用的匹配演算法。品质差或弄脏的列印件可能会导致假阴性或假阳性。 AFIS 的有效性取决于指纹资料库的大小和品质。如果指纹不在资料库中，该技术就无法辨识一个人。当处理庞大的资料库时，匹配指纹可能非常耗时。即时配对可能并不总是可行。

储存指纹资料会引发隐私问题，并且存在敏感资讯可能被存取、窃取或滥用的风险。实施和维护 AFIS 系统可能非常昂贵，因此小型组织或发展中国家不太容易使用该系统。环境条件会影响指纹影像的品质。潮湿、骯脏或受损的手指可能无法提供清晰的指纹。

Overview

Global Automated Fingerprint Identification Systems Market reached US$ 8.5 billion in 2022 and is expected to reach US$ 67.8 billion by 2030, growing with a CAGR of 23.2% during the forecast period 2023-2030.

Growing security threats and the need for reliable identification and authentication methods have driven the adoption of automated fingerprint identification systems in various sectors, that includes law enforcement, border control and access control. Advancements in technology in fingerprint recognition, which include accurate and faster fingerprint-matching algorithms, which have made automated fingerprint identification system systems more efficient and reliable.

Fingerprint recognition is a being accepted biometric authentication method and its adoption is growing in applications like mobile devices, financial transactions and identity verification. Many governments worldwide have implemented automated fingerprint identification systems for national ID programs, passport control and criminal identification databases, contributing to the growth of the automated fingerprint identification systems market.

In 2022, Asia-Pacific is expected to be the fastest growing region in the global automated fingerprint identification systems market having around 1/4th of the market. Governments and organizations across the region are increasingly adopting biometric solutions for various purposes, including law enforcement, border control, national ID programs and access control.

Dynamics

Rising Government Processes

Automated fingerprint identification systems provide a high level of security and accurately verify individual's identities through their fingerprints and this advancement is crucial for national security, border control and law enforcement. It helps law agencies to solve crimes by identifying fingerprints found in crime scenes with the database, which also leads to identifying criminals with multiple identities.

For instance, on 12 August 2023, The National Automated Fingerprint Identification System (NAFIS) team of the National Crime Records Bureau (NCRB) in India received the Gold Award under the Excellence in Government Process Reengineering for Digital Transformation Category-1 from the Department of Administrative Reforms and Public Grievances (DARPG).

Union Home Minister and Minister of Cooperation, Shri Amit Shah, congratulated the NAFIS team for this achievement. Shri Amit Shah commended the NAFIS team's dedication to creating a fool-proof fingerprint identification system, aligning with Prime Minister Shri Narendra Modi's vision of a secure India.

Growing Concerns for Payment Security

Automated fingerprint identification systems are used to enhance payment security by enabling biometric authentication methods like fingerprint recognition. Consumers link their payment accounts to their fingerprints which makes it extremely difficult for unauthorized users that access their financial information. Users authenticate with two factor authentication process for online payments.

For instance, on 5 September 2023, Huawei Mobile Services formed strategic partnerships with several leading banks in the United Arab Emirates to enhance the digital banking landscape in the region. The collaborations include banks such as ADCB, ENBD, FAB, Mashreq, ADIB and Standard Chartered Bank UAE and these partnerships aim to offer Huawei users a broader range of financial services, enabling them to access their accounts and conduct payments through banking apps available on the Huawei AppGallery.

Technology Advancement

The continuous development in technology of biometric technologies includes fingerprint recognition which has played a significant role in the growth of the market. It provides a high level of security by accurately verifying individual identities through fingerprints which makes valuable tools for enhancing security in both sectors, private and public. Also, it enables quick and accurate fingerprint matches.

For instance, on 6 June 2023, Fingerprint, a device intelligence platform, unveiled Fingerprint Pro Plus, which introduces Smart Signals, an innovation designed to enhance fraud prevention efforts. Smart Signals provides real-time, actionable intelligence that builds on Fingerprint's browser and device identification signals, currently used by over 6,000 companies for fraud prevention.

The technology enables platforms and decision engines used by companies to adapt quickly to changes in browser and mobile application technology, improving accuracy in identifying fraudulent activities.

Implementing and Maintaining is a Complex Process

The accuracy depends on the quality of the fingerprint images and the matching algorithms used. Poor-quality or smudged prints can result in false negatives or positives. The effectiveness of AFIS relies on the size and quality of the fingerprint database. The technology cannot identify a person if their fingerprints are not in the database. When dealing with a huge database, matching fingerprints can be time-consuming. Real-time matching may not always be feasible.

Storing fingerprint data raises privacy concerns and there is a risk that this sensitive information could be accessed, stolen or misused. Implementing and maintaining an AFIS system can be expensive, making it less accessible for smaller organizations or developing countries. Environmental conditions can affect the quality of fingerprint images. Wet, dirty or damaged fingers may not provide clear prints.

Segment Analysis

The global automated fingerprint identification systems market is segmented based component, search type, application and region.

Growing Adoption of Software in Automated Finger Identification Systems

Software component is expected to be the dominant segment with about 1/3rd of the market during the forecast period. The rise in crime rates, especially in urban areas, drives the demand for more efficient and accurate fingerprint identification systems. Law enforcement agencies require advanced AFIS software to solve crimes and identify suspects quickly.

According to the report by geeksforgeeks organization, more than 95% of the country's 16,098 police stations use the Crime and Criminal Tracking Network & Systems software and 97% have established connectivity. For instance, on 17 August 2022, The deployment of the National Automated Fingerprint Identification System (NAFIS) in India is a significant development in the country's law enforcement and criminal investigation efforts.

NAFIS is linked to the Crime and Criminal Tracking Network & Systems database and this integration ensures that every person arrested and recorded in CCTNS receives a unique identifier, which aids in tracking and identifying individuals involved in criminal activities.

Geographical Penetration

Rising Law Enforcement in North America

North America is among the growing regions in the global automated fingerprint identification systems market with round 1/3rd of the market in 2022. Owing to the rapid use in law enforcement and criminal justice systems, the region is the primary driver of the rise of automated fingerprint identification systems. Police departments, forensic labs and other organisations rely on automated fingerprint identification systems to identify criminals, solve crimes and manage crime databases.

For example, on March 9, 2023, IDEMIA, a global provider of secure identification solutions, expanded its partnership with Florida's Department of Law Enforcement to deliver a cloud-based Multi-Biometric Identification System. The solution is based on an automated biometric identification system that supports criminal investigators and law enforcement officers in assessing different biometric data types including fingerprints, palm prints and latent hand prints.

Competitive Landscape

The major global players in the market include: THALES, IDEMIA, SecuGen Corporation, Innovatrics, Aware Inc., Suprema, Synaptics incorporated, DERMALOG Identification Systems GmbH, Precise biometrics, HID global Corporation.

COVID-19 Impact Analysis

The pandemic disrupted the normal operations of law enforcement agencies and government offices, including those responsible for maintaining and operating automated fingerprint identification systems. Lockdowns, social distancing measures and reduced staffing affected the ability to process fingerprint data efficiently. The use of personal protective equipment (PPE), such as gloves, by law enforcement officers and fingerprint examiners became necessary during the pandemic.

Many government employees, including those working with automated fingerprint identification systems, had to adapt to remote work arrangements and this transition could have posed challenges in terms of accessing and managing fingerprint databases securely. The closure or limited operations of courts during the pandemic led to significant backlogs of criminal cases. As a result, fingerprint identification requests and caseloads for automated fingerprint identification systems operators may have increased.

Law enforcement agencies worldwide shifted their priorities during the pandemic to address public health and safety concerns related to COVID-19, this shift in focus may have affected the allocation of resources to AFIS-related projects and upgrades. The pandemic accelerated the adoption of touchless biometric authentication methods, such as facial recognition and iris scanning, in various applications and this could potentially impact the demand for traditional fingerprint-based systems.

AI Impact

AI has improved the accuracy of fingerprint-matching algorithms used in automated fingerprint identification systems. Machine learning and deep learning techniques have made it possible to identify matches even in cases with low-quality or partial fingerprint images. AI has accelerated the matching process in automated fingerprint identification systems, allowing for quicker identification of individuals and this is especially crucial in law enforcement and border control scenarios.

AI-powered automated fingerprint identification systems can handle larger databases of fingerprints efficiently, this scalability is vital as the volume of fingerprint data continues to grow. AI helps in reducing false positives and negatives, leading to more reliable results in identifying individuals and this is essential in criminal investigations and security applications.

For instance, on 31 August 2023, Google introduced an innovative watermarking solution to safeguard the authenticity of AI-generated images. The system integrates subtle watermarks into AI-generated images, serving as digital signatures to indicate that the images were created by artificial intelligence algorithms and this development is in response to concerns about the potential misuse and misrepresentation of AI-generated visuals, which have become increasingly difficult to distinguish from genuine photographs.

Russia- Ukraine War Impact

In regions affected by conflict, government agencies and law enforcement organizations may experience disruptions in their normal operations, including those related to automated fingerprint identification systems, this could lead to delays in fingerprint identification and criminal investigations. In times of conflict, there may be increased concerns about the security of sensitive biometric data stored in automated fingerprint identification systems databases.

During a war or conflict, government resources may be redirected toward immediate security and defense needs and this could affect the allocation of resources to maintain and upgrade systems. In cases where international collaboration on criminal investigations is necessary, geopolitical tensions resulting from the conflict could hinder cooperation between law enforcement agencies that rely on systems data sharing.

By Component

Software
Hardware

By Search Type

Tenprint Search
Latent Search

By Application

Commercial
Governments
Banking & Finance
Healthcare
Hospitality
Others

By Region

North America
- U.S.
- Canada
- Mexico

Europe
- Germany
- UK
- France
- Italy
- Russia
- 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

On 15 May 2022, the Maharashtra government launched an Automated Multimodal Biometric Identification System aimed at improving crime detection and conviction rates and this advanced system stores fingerprints, palmprints, facial scans and eye scans of criminals and suspects digitally. It is designed to assist law enforcement agencies in identifying and tracking criminals using various biometric data, including palmprints and facial recognition.
On 22 January 2021, Fujitsu Laboratories introduced a multi-factor biometric authentication system aimed at facilitating contactless shopping in the post-COVID-19 era, this solution combines two forms of biometric authentication: facial verification, even when users are wearing masks and palm recognition.
On 5 June 2023, Pakistan's National Database Registration Authority (NADRA) introduced iris recognition technology in several cities to enhance the existing biometric verification system. Iris recognition is known for its high reliability and accuracy in identification. NADRA stated that this technology will complement the automated fingerprint identification system introduced over a decade ago and the facial recognition systems.

Why Purchase the Report?

To visualize the global automated fingerprint identification systems market segmentation based on component, search type, application 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 automated fingerprint identification systems market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global automated fingerprint identification systems market report would provide approximately 61 tables, 58 figures and 202 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 Component
3.2. Snippet by Search Type
3.3. Snippet by Application
3.4. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Rising Government Processes
  - 4.1.1.2. Growing Concerns for Payment Security
  - 4.1.1.3. Technology Advancement
- 4.1.2. Restraints
  - 4.1.2.1. Implementing and Maintaining is a Complex Process
- 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 Component

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Component
- 7.1.2. Market Attractiveness Index, By Component
7.2. Software*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Hardware

8. By Search Type

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Search Type
- 8.1.2. Market Attractiveness Index, By Search Type
8.2. Tenprint Search*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. Latent Search

9. By Application

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 9.1.2. Market Attractiveness Index, By Application
9.2. Managed*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Governments
9.4. Banking & Finance
9.5. Healthcare
9.6. Hospitality
9.7. Others

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 Component
- 10.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Search Type
- 10.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 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 Component
- 10.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Search Type
- 10.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 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. Russia
  - 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 Component
- 10.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Search Type
- 10.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 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 Component
- 10.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Search Type
- 10.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 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 Component
- 10.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Search Type
- 10.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application

11. Competitive Landscape

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

12. Company Profiles

12.1. THALES*
- 12.1.1. Company Overview
- 12.1.2. Product Portfolio and Description
- 12.1.3. Financial Overview
- 12.1.4. Key Developments
12.2. IDEMIA
12.3. SecuGen Corporation
12.4. Innovatrics
12.5. Aware Inc.
12.6. Suprema
12.7. Synaptics incorporated
12.8. DERMALOG Identification Systems GmbH
12.9. Precise biometrics
12.10. HID global Corporation

LIST NOT EXHAUSTIVE