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生物辨別技術

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1352157

全球自動指紋辨識系統市場 - 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個工作天內

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簡介目錄

概述

全球自動指紋辨識系統市場在 2022 年達到 85 億美元，預計到 2030 年將達到 678 億美元，2023-2030 年預測期間複合年成長率為 23.2%。

日益成長的安全威脅以及對可靠識別和身份驗證方法的需求推動了執法、邊境控制和訪問控制等各個領域採用自動指紋識別系統。指紋辨識技術的進步，包括準確和更快的指紋匹配演算法，使得自動指紋辨識系統更有效率和可靠。

指紋辨識是一種被接受的生物辨識認證方法，其在行動裝置、金融交易和身份驗證等應用中的採用正在不斷成長。全球許多政府已為國家身分證計劃、護照控制和犯罪識別資料庫實施了自動指紋識別系統，促進了自動指紋識別系統市場的成長。

到2022年，亞太地區預計將成為全球自動指紋辨識系統市場成長最快的地區，約佔市場的1/4。該地區的政府和組織擴大將生物識別解決方案用於各種目的，包括執法、邊境管制、國家身分識別計劃和存取控制。

動力學

不斷發展的政府流程

自動指紋辨識系統提供高水準的安全性，並透過指紋準確驗證個人身份，這項進步對於國家安全、邊境管制和執法至關重要。它透過資料庫識別犯罪現場發現的指紋，幫助法律機構解決犯罪問題，這也有助於識別具有多重身分的罪犯。

例如，2023 年 8 月 12 日，印度國家犯罪記錄局 (NCRB) 的國家自動指紋識別系統 (NAFIS) 團隊榮獲印度商務部頒發的數位化轉型政府流程再造卓越一類金獎。行政改革與公眾申訴（DARPG ）。

聯邦內政部長兼合作部長 Shri Amit Shah 祝賀 NAFIS 團隊的這項成就。 Shri Amit Shah 讚揚了 NAFIS 團隊致力於創建萬無一失的指紋識別系統，這符合印度總理莫迪 (Shri Narendra Modi) 的安全願景。

人們對支付安全的擔憂日益加深

自動指紋辨識系統透過啟用指紋辨識等生物辨識方法來增強支付安全性。消費者將他們的支付帳戶與指紋關聯起來，這使得未經授權的用戶很難存取他們的財務資訊。使用者透過線上支付的兩因素身份驗證流程進行身份驗證。

例如，2023年9月5日，華為行動服務與阿拉伯聯合大公國多家領先銀行建立策略合作夥伴關係，以提升該地區的數位銀行格局。這些合作包括 ADCB、ENBD、FAB、Mashreq、ADIB 和阿拉伯聯合大公國渣打銀行等銀行，這些合作夥伴關係旨在為華為用戶提供更廣泛的金融服務，使他們能夠透過銀行應用程式存取自己的帳戶並進行支付。華為應用市場。

技術進步

生物辨識技術的不斷發展，包括指紋識別，在市場的成長中發揮了重要作用。它透過指紋準確地驗證個人身份，提供高水準的安全性，這為增強私人和公共部門的安全性提供了寶貴的工具。此外，它還可以實現快速、準確的指紋匹配。

例如，2023 年 6 月 6 日，裝置智慧平台 Fingerprint 推出了 Fingerprint Pro Plus，其中引入了智慧訊號，這是一項旨在加強詐欺預防工作的創新。 Smart Signals 提供基於 Fingerprint 瀏覽器和裝置識別訊號的即時、可操作的智慧，目前有 6,000 多家公司使用該訊號來預防詐騙。

該技術使該公司使用的平台和決策引擎能夠快速適應瀏覽器和行動應用程式技術的變化，從而提高識別詐欺活動的準確性。

實施和維護是一個複雜的過程

準確性取決於指紋影像的品質和所使用的匹配演算法。品質差或弄髒的列印件可能會導致假陰性或假陽性。 AFIS 的有效性取決於指紋資料庫的大小和品質。如果指紋不在資料庫中，該技術就無法辨識一個人。當處理龐大的資料庫時，匹配指紋可能非常耗時。即時配對可能並不總是可行。

儲存指紋資料會引發隱私問題，並且存在敏感資訊可能被存取、竊取或濫用的風險。實施和維護 AFIS 系統可能非常昂貴，因此小型組織或發展中國家不太容易使用該系統。環境條件會影響指紋影像的品質。潮濕、骯髒或受損的手指可能無法提供清晰的指紋。

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