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Biometric Sensors Market was valued at over USD 1.8 billion in 2023 and is estimated to register a CAGR of over 12.5% between 2024 and 2032. The biometric authentication industry has been experiencing an upward trend, boosting the growth of the market. Various organizations from finance, health and government sectors have adopted biometric authentication systems to prevent security breaches and identity theft.
For instance, in January 2024, Mastercard launched the Mastercard Biometric Authentication Service that was designed to improve safety as well as convenience for customers. This service confirms identities by means of fingerprint, iris or facial recognition scanning so as to protect personal information better.
A favorable regulatory environment is a significant driver of growth for the biometric sensors industry. Governments and industry groups have established guidelines to improve security such as Europe’s GDPR, or India’s Aadhaar system. To conform to these regulations, companies must incorporate biometric verification and other identification systems, therefore fueling demand for biometric sensors across diverse sectors. Abiding by these standards facilitates interoperability, dependability and safety of the technology, therefore fostering trust among users.
Report Attribute | Details |
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Base Year: | 2023 |
Biometric Sensors Market Size in 2023: | USD 1.8 Billion |
Forecast Period: | 2024 to 2032 |
Forecast Period 2024 to 2032 CAGR: | 12.5% |
2032 Value Projection: | USD 5.1 Billion |
Historical Data for: | 2018 to 2023 |
No. of Pages: | 230 |
Tables, Charts & Figures: | 362 |
Segments covered: | Product, application, end-use and region |
Growth Drivers: |
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Pitfalls & Challenges: |
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The biometric sensors market faces significant obstacles in the field of liveness detection which is one major component of biometric authentication systems that are designed to fight against spoofing attacks. Security and reliability of biometric authentication systems is under serious threat from various spoofing techniques like using fake biometrics. Therefore, the need to develop robust liveness detection algorithms capable of differentiating between real and fake biometric samples. Success of the algorithm depends mainly on reducing false negatives while combating several tricks as well as adapting itself to changing environments.