The LUTBIO database provides a comprehensive resource for research in multimodal biometric authentication, featuring the following key aspects:

• Extensive Biometric Modalities: The database contains data from nine biometric modalities: voice, face, fingerprint, contact-based palmprint, electrocardiogram (ECG), opisthenar (back of hand), ear, contactless palmprint, and periocular region.

• Diverse Demographics: Data were collected from 306 individuals, with a balanced gender distribution of 164 males and 142 females, spanning an age range of 8 to 90 years. This diverse age representation enables analyses across a wide demographic spectrum.

• Representative Population Sampling: Volunteers were recruited from naturally occurring communities, ensuring a large-scale, statistically representative population. The collected data encompass variations observed in real-world environments.

• Support for Multimodal and Cross-Modality Research: LUTBIO provides both contact-based and contactless palmprint data, as well as fingerprint data (from optical images and scans), promoting advancements in multimodal biometric authentication. This resource is designed to guide the development of future multimodal databases.

• Flexible, Decouplable Data: The biometric data in the LUTBIO database are designed to be highly decouplable, enabling independent processing of each modality without loss of information. This flexibility supports both single-modality and multimodal analysis, empowering researchers to optimize, combine, and customize biometric features for specific applications.

✅ Data Availability: If you wish to use the LUTBIO dataset, please download the attached Word document, fill in the information, and send it as an attachment to rykeryang AT 163.com. We will process your request as soon as possible!

🥸 Important Notice: Please read the data collection protocol of the LUTBIO dataset carefully before use, as it is essential for understanding and correctly interpreting the dataset. Thank you.

😎 Good news! Our paper has been accepted by Information Fusion, and the DOI is https://doi.org/10.1016/j.inffus.2025.102945. We appreciate the reviewers and the editor for their efforts.🥰🥰🥰

The global Biometric Database Management Services market is experiencing robust growth, driven by the increasing adoption of biometric authentication across various sectors. The market's expansion is fueled by the rising need for enhanced security, improved user experience, and the growing demand for efficient identity management solutions in government, healthcare, finance, and law enforcement. Technological advancements, such as the development of more sophisticated algorithms and cloud-based solutions, are further accelerating market growth. The market is segmented by deployment type (cloud-based and on-premise), application (access control, time and attendance, border control, and others), and end-user (government, healthcare, finance, and others). Companies like Aware, Thales, and IDEMIA are leading the market, offering comprehensive solutions that cater to diverse customer needs. However, concerns regarding data privacy and security, along with the high initial investment costs associated with implementing biometric systems, pose challenges to market growth. The market is expected to maintain a steady Compound Annual Growth Rate (CAGR) throughout the forecast period (2025-2033), with significant regional variations based on technological adoption rates and regulatory frameworks. The competitive landscape is characterized by a mix of established players and emerging technology providers. Established players are focusing on strategic partnerships and acquisitions to expand their market reach and product offerings. Emerging players are innovating with advanced technologies such as AI-powered biometrics and blockchain integration to enhance security and efficiency. Furthermore, the increasing adoption of cloud-based solutions is driving down costs and improving scalability, making biometric database management services more accessible to a wider range of organizations. The market's future growth will depend heavily on the evolution of data privacy regulations, the continued development of more accurate and secure biometric technologies, and the increasing acceptance of biometric authentication among end-users. The forecast period anticipates significant growth across all segments, with cloud-based solutions and government applications leading the charge.

Tabular dataset of data collected from chinook salmon smolt trapping on the Chena River in May and June of 2017-2019. Trapping took place between the East end of the Fort Wainwright airfield (N 64.831952, W -147.571279) and adjacent to Chena Marina (N 64.81065, W -147.89908) in 2017, between Hamilton Acres (N 64.839650, W -147.681000) and Pikes Landing (N 64.830467, W –147.848750) in 2018, and between Nordale Road Bridge (N64.846536, W -147.410418) and Grahel Park (N 64.845831, W –147.706114) in 2019. This dataset contains the biometric and tagging data related to captured Chinook salmon.

SAIVT Soft Biometric Database

Overview

The SAIVT-SoftBio database contains a collection of multi-camera sequences of 152 pedestrians captured from a set of 8 surveillance cameras. This database provides a challenging and realistic test-bed for person redetection tasks, and is freely available for download. Contact Dr Simon Denman (s dot denman at qut dot edu dot au) for more information.

Licensing

The SAIVT-SOFTBIO database is © 2012 QUT and is licensed under the Creative Commons Attribution-ShareAlike 3.0 Australia License (http://creativecommons.org/licenses/by-sa/3.0/).

Attribution

To attribute this database, use the citation provided on our publication at [eprints] (http://eprints.qut.edu.au/53437/).

Acknowledgement in publications

In addition to citing our paper, we request the following text be included in your publications:
'We would like to thank the SAIVT Research Labs at Queensland University of Technology (QUT) for freely supplying us with the SAIVT-SoftBio database for our research'.

Installing the SAIVT-SoftBio Database

Download, join, and unpack the four files (and optionally the motion segmentation masks).

Once the main archive has been uboacked, you should have the following data structure and the SAIVT-SoftBio database is installed:

​SAIVT-SoftBio
+-- Calibration
  +-- C1--U1-17
  +-- C2--U18-48
  ...
  +-- C10--U140-152
+-- Uncontrolled
  +-- Subject001
  +-- Subject002
  +-- Subject003
  ...
  +-- Subject152
+-- Bialkowski2012 - A database for person re-identification in multi-camera surveillance networks.pdf
+-- LICENSE.txt
+-- README.txt
+-- SAIVTSoftBioDatabase.xml

The 'Calibration' directory contains a camera calibration and background images (one image per camera) for the dataset. It is arranged into groups of subjects (i.e. C1--U1-17 contains camera calibration and background images valid for subjects 1 to 17). All camera calibration has been calculated using Tsai's method.
The 'Uncontrolled' directory contains the image sequences for each subject, arranged by camera view.

The 'SAIVTSoftBioDatabase.xml' file defines the database. This file specifies the number of cameras used and number of calibrations present, the regions of interest for each camera (

More information on the SAIVT-SOFTBIO database in our paper at QUT eprints (http://eprints.qut.edu.au/53437/)

Global Biometric Technology Market size is set to expand from $ 48.48 Billion in 2023 to $ 157.64 Billion by 2032, with an anticipated CAGR of around 14% from 2024 to 2032.

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Biometrics-As-A-Service Market Size 2024-2028

The biometrics-as-a-service market size is forecast to increase by USD 3.5 billion at a CAGR of 19.46% between 2023 and 2028.

The market is experiencing significant growth due to increasing security and surveillance needs across various industries, particularly In the BFSI sector. The adoption of advanced biometric technologies such as face and voice recognition is on the rise, offering enhanced security and convenience. However, privacy concerns remain a major challenge, with consumers expressing apprehensions regarding the use of their biometric data. Addressing these concerns through robust data security measures and transparent data handling practices is essential for market growth. Additionally, the integration of biometric systems with IoT devices and the emergence of contactless biometric solutions are key trends shaping the market.Overall, the market is poised for growth, driven by the need for secure and convenient identification solutions.

What will be the Size of the Biometrics-As-A-Service Market During the Forecast Period?

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The market is experiencing significant growth due to increasing demands for advanced security solutions in response to terrorist and theft activities. Biometric technologies, such as fingerprint sensors and facial recognition, are becoming crucial components of national security and e-passport programs worldwide. Government support and investment in contactless biometric solutions have fueled the adoption of these technologies in criminal identification systems and security systems. The Android platform and touch-based technologies are driving the integration of biometrics into everyday devices, making advanced authentication services more accessible. The cloud is facilitating the deployment and management of biometric solutions, enabling contact-free sensing and reducing the need for physical infrastructure.Biometric shopper analytics and loss prevention solutions are also gaining traction in retail and commercial sectors, providing valuable insights and enhancing personal data security. The coronavirus pandemic has further accelerated the need for touch-free and contact-free sensing solutions, as businesses seek to minimize physical contact and maintain social distancing. Advanced biometric technologies, including facial recognition and advanced authentication services, continue to evolve, offering more accurate and efficient solutions for border patrol agents and other security applications. Overall, the market is poised for continued growth, driven by the need for enhanced security and data protection.

How is this Biometrics-As-A-Service Industry segmented and which is the largest segment?

The biometrics-as-a-service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2017-2022 for the following segments. ApplicationSite access controlTime recordingMobile applicationWeb and workplaceModalityUnimodalMultimodalGeographyNorth AmericaUSAPACChinaJapanEuropeGermanyUKSouth AmericaMiddle East and Africa

By Application Insights

The site access control segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth, particularly In the segment of biometric site access control systems. This expansion is driven by escalating security threats, data breaches, and criminal activities, leading to increased demand for advanced security measures. Biometric site access control systems are being deployed across various industries, including healthcare, finance, transportation, and government, to bolster security and prevent unauthorized access. Government initiatives in countries like India, Australia, China, the UK, and the US, aimed at implementing biometric site access control systems for enhanced security, are also gaining traction. Contactless biometric solutions, such as iris recognition and touchless technologies, are increasingly popular due to the ongoing pandemic.Biometric-as-a-Service (BaaS) solutions offer cost-effective, cloud-based biometrics management, making them an attractive alternative to traditional methods. Biometric capture devices, integration services, and low-cost solutions are essential components of BaaS offerings. Biometric onboarding, authentication capabilities, and multi-modal systems are also key features of these solutions. Business applications, including healthcare, enterprise, automotive, imaging, financial services, legal, manufacturing, education, logistics, and retail, are benefiting from the adoption of BaaS solutions. Behavioral biometrics and multi-factor authentication solutions are also gaining popularity for added security.

Get a glance at the Biometrics-As-A-Service Industry report of share of various segments Req

The size of the Biometric Database Management Services market was valued at USD XXX million in 2024 and is projected to reach USD XXX million by 2033, with an expected CAGR of XX % during the forecast period.

The FaciaVox dataset is an extensive multimodal biometric resource designed to enable in-depth exploration of face-image and voice recording research areas in both masked and unmasked scenarios.

Features of the Dataset:

1. Multimodal Data: A total of 1,800 face images (JPG) and 6,000 audio recordings (WAV) were collected, enabling cross-domain analysis of visual and auditory biometrics.

2. Participants were categorized into four age groups for structured labeling:
Label 1: Under 16 years
Label 2: 16 to less than 31 years
Label 3: 31 to less than 46 years
Label 4: 46 years and above

3. Sibling Data: Some participants are siblings, adding a challenging layer for speaker identification and facial recognition tasks due to genetic similarities in vocal and facial features. Sibling relationships are documented in the accompanying "FaciaVox List" data file.

4. Standardized Filenames: The dataset uses a consistent, intuitive naming convention for both facial images and voice recordings. Each filename includes:
Type (F: Face Image, V: Voice Recording)
Participant ID (e.g., sub001)
Mask Type (e.g., a: unmasked, b: disposable mask, etc.)
Zoom Level or Sentence ID (e.g., 1x, 3x, 5x for images or specific sentence identifier {01, 02, 03, ..., 10} for recordings)

5. Diverse Demographics: 19 different countries.

6. A challenging face recognition problem involving reflective mask shields and severe lighting conditions.

7. Each participant uttered 7 English statements and 3 Arabic statements, regardless of their native language. This adds a challenge for speaker identification.

Research Applications

FaciaVox is a versatile dataset supporting a wide range of research domains, including but not limited to:
• Speaker Identification (SI) and Face Recognition (FR): Evaluating biometric systems under varying conditions.
• Impact of Masks on Biometrics: Investigating how different facial coverings affect recognition performance.
• Language Impact on SI: Exploring the effects of native and non-native speech on speaker identification.
• Age and Gender Estimation: Inferring demographic information from voice and facial features.
• Race and Ethnicity Matching: Studying biometrics across diverse populations.
• Synthetic Voice and Deepfake Detection: Detecting cloned or generated speech.
• Cross-Domain Biometric Fusion: Combining facial and vocal data for robust authentication.
• Speech Intelligibility: Assessing how masks influence speech clarity.
• Image Inpainting: Reconstructing occluded facial regions for improved recognition.

Researchers can use the facial images and voice recordings independently or in combination to explore multimodal biometric systems. The standardized filenames and accompanying metadata make it easy to align visual and auditory data for cross-domain analyses. Sibling relationships and demographic labels add depth for tasks such as familial voice recognition, demographic profiling, and model bias evaluation.

The booming biometric data management software market is projected to reach $6.1 billion by 2033, driven by rising security concerns and digital transformation. Explore key trends, market size, leading companies like Aware, IDEMIA, and Thales, and regional growth in this comprehensive analysis.

2D and 3D Fingerrpint dataset. It was created by former Ph.D. stduent Ms. Wei Zhou under the supervision of Prof. J. Hu who acts as the communication contact., , , # 2D and 3D Fingerprint Dataset

This database consists of 3D fingerprints (2D equivalent ones) collected from 150 subjects (volunteers), so there are in total 150 subfolders.

Description of the data and file structure

For 3D Database:

1. This database consists of 3D fingerprints (2D equivalent ones) collected from 150 subjects (volunteers), so there are in total 150 subfolders.

2. The naming for the samples is SIRE-Subject ID_Finger ID_Capture Order. The Finger IDs are listed below. There are in total 150 volunteers, so the Subject ID ranges from 1 to 150. For each subject, the Finger ID ranges from 1 to 10 accordingly. The Capture Order can be 1 or 2 for 3D fingerprints. For example, the fingerprint image named SIRE-102_3_2.bmp in subfolder 102 represents the second capture of the right middle finger of subject 102. Besides, there are several post-processed images (e.g. SIRE-1_1_1_HT1.bmp, SIRE-1_1_1_R414.bmp) corresponding to each 3D fingerprint image.

3. In the sub subfold...

Snapshot img

The mobile biometrics market share is expected to increase by USD 12.56 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 11.59%.

This mobile biometrics market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers mobile biometrics market segmentation by technology (fingerprint recognition, face recognition, voice recognition, and others), application (access control, mobile payment, and authentication), and geography (APAC, North America, Europe, South America, and MEA). The mobile biometrics market report also offers information on several market vendors, including Egis Technology Inc., Fingerprint Cards AB, Fujitsu Ltd., M2SYS Technology, NEC Corp., Precise Biometrics AB, Shenzhen Goodix Technology Co. Ltd., Synaptics Inc., Thales Group, and Verint Systems Inc. among others.

What will the Mobile Biometrics Market Size be During the Forecast Period?

Download Report Sample to Unlock the Mobile Biometrics Market Size for the Forecast Period and Other Important Statistics

Mobile Biometrics Market: Key Drivers, Trends, and Challenges

The demand for m-commerce is notably driving the mobile biometrics market growth, although factors such as the need to comply with stringent regulations and standards may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the mobile biometrics industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Mobile Biometrics Market Driver

Demand for m-commerce is one of the key drivers of the market in focus. The thriving demand for m-commerce will remain one of the prime factors driving the global mobile biometrics market. Over the last few years, there has been an increase in the number of mobile payment transactions globally, fueled by the digitization of banks. Furthermore, mobile wallet enterprises such as Google and e-commerce companies such as Amazon, eBay Inc., and Alibaba have started spreading their presence globally by offering various discounts to their customers. Telecommunication service providers have also started offering mobile wallet services for various payments such as utility bill payments and mobile phone recharge. For instance, in May 2019, Mobitel, a Sri Lankan telecommunication service provider, announced a partnership with MasterCard International Inc. to enhance its mobile money platform called mCash. Similarly, in May 2019, Fitbit Inc., a health and fitness technology solutions provider, announced that users of Fitbit Ionic, Fitbit Versa, and Fitbit Charge 3 in Singapore could use Fitbit Pay on their devices to pay for public transport. Furthermore, in March 2019, Apple announced the launch of the Apple Card, a credit card designed for iPhone users.

Key Mobile Biometrics Market Trend

Behavioral biometrics is one of the key trends in the market in focus. Unusual patterns of users' behavior can be detected using behavioral biometrics, and the transaction in progress can be blocked. Behavioral biometrics offers an advanced risk management process by ensuring multiple layers of security, i.e., device, user behavior, and location specifics. This enables enterprises, especially financial institutions, to quickly detect any fraudulent activity. Behavioral biometrics plays a key role in identifying automated bots in mobile apps. Also, vendors in the global mobile biometrics market are building authentic behavioral models based on the data from biometric sensors and human-device interaction with the help of ML. In March 2019, BioCatch Ltd., a behavioral biometric solution provider, announced that it had collaborated with a mobile identity service provider known as Entersekt Proprietary Ltd. The collaboration would enable Entersekt to integrate BioCatch s behavioral biometric technology into its mobile identity services platform. Therefore, in the near future, behavioral biometrics is set a play a prominent role in the global mobile biometrics market, adding a new layer of security.

Key Mobile Biometrics Market Challenge

The need to comply with stringent regulations and standards is a major hindrance to the market focus. The cost of biometric sensors remains one of the key challenges for vendors in the global mobile biometrics market. As the price of mobile devices is gradually decreasing over the last few years, mobile device manufacturers are reducing their manufacturing costs to attain profits and remain competitive in the market. This is a challenge in emerging countries such as India, where the low-income group contributes a majority share of the purchases. In 2018, the price of capacitive sensors declined rapidly compared with the previous

Market Research Intellect's Biometric Data Encryption Device Market Report highlights a valuation of USD 4.2 billion in 2024 and anticipates growth to USD 9.1 billion by 2033, with a CAGR of 9.5% from 2026-2033.Explore insights on demand dynamics, innovation pipelines, and competitive landscapes.

Privacy‑Preserving Biometrics Market Outlook

As per our latest research, the global privacy-preserving biometrics market size in 2024 stands at USD 2.3 billion, with a robust compound annual growth rate (CAGR) of 18.1% anticipated through the forecast period. This growth trajectory is expected to propel the market to reach approximately USD 11.9 billion by 2033. The market’s expansion is primarily fueled by the surging demand for advanced biometric security solutions that safeguard user privacy amidst escalating cyber threats and intensifying regulatory pressures worldwide.

One of the most significant growth drivers for the privacy-preserving biometrics market is the increasing emphasis on data protection and privacy regulations globally. Legislation such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and other similar frameworks across Asia Pacific and Latin America have compelled organizations to adopt biometric systems that not only authenticate individuals securely but also ensure that sensitive biometric data is protected from misuse or unauthorized access. This regulatory landscape has accelerated the adoption of privacy-enhancing technologies such as homomorphic encryption, secure multi-party computation, and federated learning within biometric applications, thereby fostering market growth.

Another crucial growth factor is the rising incidences of identity theft, data breaches, and sophisticated cyberattacks targeting biometric databases. As organizations across sectors such as banking, healthcare, government, and retail increasingly leverage biometrics for authentication, the risk of biometric data exploitation has become a critical concern. Privacy-preserving biometrics address these vulnerabilities by ensuring that raw biometric data is never exposed, stored, or transmitted in a form that can be reverse-engineered or compromised. This heightened focus on secure biometric modalities and advanced cryptographic techniques is driving the rapid integration of privacy-preserving solutions in both legacy and new biometric systems.

Technological advancements and the proliferation of digital transformation across industries are also catalyzing market expansion. Innovations in artificial intelligence, machine learning, and edge computing have enabled the development of highly accurate, scalable, and privacy-centric biometric systems. These solutions facilitate seamless user experiences while maintaining stringent privacy standards. The increasing adoption of cloud-based biometric services, particularly among small and medium enterprises (SMEs), further amplifies market growth by providing cost-effective, scalable, and easily deployable privacy-preserving biometric solutions.

From a regional perspective, North America currently leads the privacy-preserving biometrics market, driven by early technology adoption, stringent regulatory frameworks, and high investment in cybersecurity infrastructure. However, Asia Pacific is projected to exhibit the fastest growth over the forecast period, owing to rapid urbanization, increased digitalization, and government-led initiatives to enhance public safety and digital identity infrastructure. Europe remains a significant contributor, buoyed by strong regulatory compliance and robust demand from the BFSI and healthcare sectors. Meanwhile, emerging markets in Latin America and the Middle East & Africa are witnessing rising adoption rates, driven by increasing awareness of privacy risks and the need for secure authentication methods.

Technology Analysis

The technology landscape of the privacy-preserving biometrics market is characterized by rapid innovation and the integration of advanced cryptographic and privacy-enhancing tools. Homomorphic encryption is gaining substantial traction, enabling computations on encrypted biometric data without exposing the raw inputs, thereby maintaining data confidentiality throughout the authentication process. Secure multi-party computation allows multiple parties to collaboratively compute a function over their inputs while keeping those inputs private, a critical feature for distributed biometric systems in sectors such as finance and healthcare where data sharing is essential yet privacy-sensitive. Differential privacy offers mathematical guarantees that individual biometric data cannot be inferred from aggregate datasets, thus ensuring comp

Contains biometric measurements (tarsus length, bill length, total head length, body mass) of birds caught at various locations in Australia (though mainly South Australia and Victoria), including data on the viral (mainly Avian Influenza Virus) analysis of cloacal and oropharyngeal swabs, serology of sera samples, and additional data on metabolites and parasite prevalence in swabs, blood cells and sera. Approximately 1500 birds (mainly waterbirds) are sampled annually.

The global biometric data management software market size was valued at USD 13.44 billion in 2025 and is projected to reach USD 40.92 billion by 2033, exhibiting a CAGR of 14.7% during the forecast period. The increasing adoption of cloud-based biometric data management solutions and the growing demand for secure and efficient identity management systems are key factors driving market growth. Cloud-based solutions offer flexibility, scalability, and cost-effectiveness, making them an attractive option for businesses and organizations. Key trends shaping the market include the emergence of AI-powered biometric technologies, such as facial recognition and voice recognition, which are improving the accuracy and convenience of biometric data management. Additionally, the rising adoption of biometrics in sectors such as banking, healthcare, and government is expected to drive demand for advanced biometric data management solutions. However, concerns about data privacy and security, as well as the potential for bias in biometric algorithms, pose challenges to market growth. Regional variations in regulatory frameworks and data protection laws also influence the adoption and implementation of biometric data management systems.

In a survey conducted in August 2024 among consumers in the United States, it was found that selfie photo was the most requested biometric data for identity proof. Around 34 percent of respondents said they have been asked to provide their selfie photo when trying to prove their identity. A further 26 percent said they were never asked for biometric information. Fingerprints were requested in 22 percent of cases, while 9 percent stated they were requested to go on a live video chat for identity proof.

The database houses North American Carbon Program data from 10 intensively monitored sites at seven stations: Bartlett Experimental Forest (New Hampshire), Fraser Experimental Forest (Colorado), Glacier Lakes Ecosystem Experiments Site (Wyoming), Marcell Experimental Forest (Minnesota), Niwot Ridge Long-term Ecological Research Site (Colorado), Silas Little Experimental Forest (New Jersey), and The Parker Tract (North Carolina). The biometric database contains both measured and estimated data. Measured data include general descriptive information and detailed measurements from 2004-2011. General descriptive information defines the sample area at the station, site, plot, and subplot level. Detailed measurements include tree, shrub, non-woody vegetation, down woody material, stump, litter, forest floor, agricultural crop, leaf area index, fine root, and soils data. Tree data were separated into three size classes: seedling, sapling, and tree. Soil data include chemistry, respiration, and water content.The two main goals of the North American Carbon Program (NACP) are to (1) develop the scientific basis to support full carbon accounting on regional and continental scales; and (2) support long-term quantitative measurements of fluxes, sources, and sinks of atmospheric CO2 and CH4, and develop forecasts for future trends.

Data reported here were collected at a network of landscape monitoring sites representing forests with different management, disturbance histories, and vegetation to bridge the gap between flux towers and national inventory programs. Key information for each site includes (1) estimates of carbon stocks and quantified impacts of management activity; (2) estimates of net ecosystem production (NEP) and changes in carbon pools; and (3) estimates of forest/atmosphere carbon fluxes. The database was developed to provide detailed, well-documented, and consistent information from a network of long-term observation sites in the United States. The design of the sampling protocol and database provide examples for applications in other regions.The first edition of these data was published in 2013 (see Cross References). The second edition has three changes relative to the first edition: 1. The database format is now MS Access 2010 (instead of being Access 2007) 2. Soil chemistry data were added for the Cedar Bridge and Silas Little sites (which previously had no soil chemistry data). 3. All seedling and shrub data at the subplot and plot level have been updated to fix an algorithm error in which summed biomass was not divided by the size of the sampled area (over-estimaing biomass). The corrected tables are: Subplot_SeedAG, Subplot_ShrubAG, Plot_SeedAG and Plot_ShrubAG in LCMS_BiometricData_Ver2_Summary_2015-06-15.accdb

Minor metadata updates on 12/12/2016.

In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded. NIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work. NIST also collected some mugshot-style face and iris images of the subjects who participated in the dry run. These data are also available for download.

The global market for Biometric Data Encryption Devices is experiencing robust growth, estimated at a market size of $7,500 million in 2025, with a projected Compound Annual Growth Rate (CAGR) of 18.5% through 2033. This significant expansion is fueled by an escalating demand for enhanced security solutions across various sectors. The increasing prevalence of sophisticated cyber threats and data breaches necessitates advanced methods for protecting sensitive biometric information. Key drivers include the widespread adoption of biometric authentication in smartphones, banking, government, and enterprise environments, all seeking to leverage the inherent uniqueness of biometric identifiers while ensuring their secure storage and transmission. The growing need for compliance with stringent data privacy regulations, such as GDPR and CCPA, further propels the market as organizations invest in robust encryption technologies for biometric data. The market is characterized by continuous innovation, with a strong emphasis on developing more efficient, secure, and user-friendly encryption solutions. The market is segmented into various applications, with Commercial and Industrial sectors demonstrating substantial adoption due to the critical need for secure access control and identity verification in these environments. Residential applications are also on an upward trajectory, driven by smart home security systems. In terms of technology, Face Recognition and Fingerprint Recognition dominate the landscape, benefiting from their widespread integration into consumer devices and access control systems. However, advancements in Iris Recognition and emerging biometric modalities are also carving out significant niches. Geographically, Asia Pacific is poised for the most dynamic growth, propelled by rapid digitalization, a burgeoning middle class, and significant investments in smart cities and secure infrastructure in countries like China and India. North America and Europe remain mature yet substantial markets, driven by strong regulatory frameworks and a high level of technological adoption. Restraints include the initial implementation costs for some advanced solutions and ongoing concerns regarding the privacy implications of widespread biometric data collection, which are being mitigated by the very encryption devices the market offers. Here's a unique report description for a Biometric Data Encryption Device market analysis, incorporating your specified elements and word counts:

This comprehensive report delves into the intricate landscape of Biometric Data Encryption Devices, offering a granular analysis of market dynamics, technological advancements, and future projections. The study spans the Study Period of 2019-2033, with a deep dive into the Historical Period (2019-2024), establishing the Base Year and Estimated Year as 2025, and extending through a robust Forecast Period of 2025-2033. The market is expected to witness significant growth, with global revenues projected to reach tens of millions of dollars by the end of the forecast period.

This dataset contains anonymized clinical and biometric records of 2,500 patients, compiled from multiple hospitals across the United Kingdom. The dataset is designed to support research and development of machine learning models that can accurately predict the onset of chronic diseases using routine medical test results.

The data reflects real-world patterns typically observed in NHS and UK hospital patients undergoing general health assessments or chronic disease screenings. It is intended for educational, research, and prototype development purposes in the field of healthcare analytics and public health AI.