生物特征识别技术与标准化研究报告（2023）-中国电子技术标准化研究院_90页_6mb

Biometric recognition technology has become crucial for identity verification in modern society due to the limitations of traditional authentication methods like passwords and ID cards. This report provides an overview of the field, including its technical developments, industry applications, standardization efforts, and policy recommendations.

Overview
Biometric recognition uses inherent physiological or behavioral traits (e.g., fingerprints, facial features, iris, vein, voice, gait, genetics, etc.) for individual identification. It is characterized by uniqueness, universality, repeatability, collectability, and acceptance. A generic biometric system includes data acquisition, signal processing, storage, matching, and decision modules. Legal frameworks globally, such as the California Facial Recognition Act (2020) and the EU’s AI Act (2023), emphasize privacy protection and ethical standards. Domestically, China's Cybersecurity Law (2017), Data Security Law (2021), and Personal Information Protection Law (2021) provide legal foundations. National policies support the integration of biometric technology with AI, blockchain, and other fields to enhance security and convenience in areas like finance, public safety, and healthcare.

Technical Status & Trends
Common biometric modalities include fingerprint, facial, iris, vein, voice, gait, genome, and signature recognition. Multi-modal fusion is emerging as a future focus. Fingerprint recognition has evolved from geometric models to deep learning, with challenges in low-quality data and security. Facial recognition, driven by neural networks, now exceeds human accuracy but faces issues like age-related performance drops and algorithmic bias. Iris recognition benefits from high stability and non-contact features, though it requires specialized equipment. Vein recognition uses near-infrared imaging, blending with other modalities for enhanced security. Voice recognition depends on audio signal analysis but is vulnerable to replay attacks. Gait and signature recognition are developing in terms of dynamic features and anti-fraud capabilities. Genome and odor recognition offer high uniqueness but face technical and ethical hurdles.

Key Technologies

1. Data Acquisition involves sensors (optical, capacitive, etc.) and optical system design for quality images. 3D imaging and HDR sensors are advancing.
2. Preprocessing includes noise reduction, alignment, and quality assessment. For example, histogram equalization boosts face/iris clarity, while filters improve fingerprint and vein image quality.
3. Modeling & Matching relies on feature extraction algorithms and standards like BioAPI. Deep learning models enhance accuracy, while frameworks like ISO/IEC 19794-5 ensure consistent data formats.
4. Anti-Spoofing detects fake samples via liveness checks (e.g., thermal data, pupil response, occlusion detection using CNNs).
5. System Evaluation includes performance metrics (e.g., FAR, FRR) and security assessments (data integrity, privacy).

Industry Applications

• Public Safety: Iris and facial recognition are used for border control (e.g., Macau’s 2023 system), access control, and surveillance. Multi-modal systems help identify large-scale populations without masks/attire changes.
• Civil Services: Iris recognition aids in medical and elderly care, while fingerprint and face recognition ensure secure access in government.
• Finance: Biometric methods (e.g., iris, face) replace traditional authentication, reducing misuse risks.
• Healthcare: Iris and DNA-based systems secure patient data and authenticate staff.
• Industry: Iris scans are applied in coal mining for personnel management.
• Justice: DNA and iris recognition assists in forensic analysis, prison management, and anti-corruption.
• New Sectors: Animal identification (DNA) and extended reality (XR) in virtual environments.

Standardization
Internationally, ISO/IEC JTC1/SC37 leads biometric standards, focusing on interoperability and data exchange (e.g., ISO/IEC 19785, 19794). In China, the SAC/TC28/SC37 committee develops national standards (e.g., GB/T 26237, GB/T 29268) and conducts testing for compliance. Over 85 national standards and 17 ongoing projects exist, alongside 140+ international standards. Activities include BioAPI specifications, anti-spoofing protocols, and data quality frameworks.

Policy Recommendations

1. Industrial Development: Promote R&D in key technologies, support cross-border standardization, and incentivize projects like large-scale security systems.
2. Standardization: Expand the national standard system, enhance international influence, and improve testing platforms.
3. Regulatory Support: Strengthen financial incentives, establish R&D funds, and manage risks (e.g., data leaks, market competition).
4. Talent Cultivation: Foster interdisciplinary education in biometric and AI fields, and attract global experts.
5. Compliance Mechanisms: Ensure data security through encryption, authentication standards, and robust anti-fraud protocols.

The report highlights biometric technology’s role in enhancing security and efficiency across sectors, while stressing the need for standardized frameworks, ethical guidelines, and innovation-driven policies to address challenges like data privacy, technical limitations, and market integration.