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.

Double-identity fingerprint is a fake fingerprint created by aligning two fingerprints for maximum ridge similarity and then joining them along an estimated cutline such that relevant features of both fingerprintsare present on either sides of the cutline. The fake fingerprint containing the features of the criminal and his innocuous accomplice can be enrolled with an electronic machine readable travel document and later used to cross the automated

NIST, working with the FBI, has digitized 888 inked fingerprint arrest cards that were in various physical conditions, from pristine to badly damaged and faded, and were collected during law enforcement professionals' duties. This database contains images of the 10 rolled fingerprint impressions, the two four-finger slap impressions (finger positions 13 and 14), the two thumb slap impressions (finger positions 11 and 12) and the segmented impressions from the slap images (13,14). The database also includes the coordinates that were used to segment the impressions from the slap fingerprint images.The cards were scanned at three different resolutions: 500, 1,000, and 2,000 pixels per inch (PPI). All three resolutions were scanned in grayscale at a depth of 8 bits-per pixel.Data available as of July 2018 is Special Database 300a, in 500 ppi with PNG formatted impressions. Data at other resolutions, in other image formats, and in other record types may be forthcoming.

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.

The global biometric data management software 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 secure and efficient identity verification and management systems in applications ranging from border control and law enforcement to healthcare and financial services. The surging demand for advanced security measures, coupled with the increasing prevalence of digital transactions and remote work, is further propelling market growth. While challenges exist, such as concerns regarding data privacy and security breaches, the market is witnessing significant innovation in areas like AI-powered biometric solutions and cloud-based data management platforms. This is leading to more efficient and secure systems, mitigating some of the inherent risks. We estimate the market size to be approximately $2 billion in 2025, growing at a compound annual growth rate (CAGR) of 15% over the forecast period (2025-2033). This growth trajectory is supported by the continuous advancements in biometric technologies, expanding applications across diverse sectors, and the increasing preference for contactless solutions. Key players in this dynamic market include Aware, Thales, NEC Corporation, Veridos, IDEMIA, and others, constantly innovating to enhance their offerings and expand their market share. Segmentation within the market is driven by deployment mode (cloud, on-premise), application (access control, time and attendance, identity verification), and end-user (government, healthcare, financial services). The competitive landscape is characterized by both established players with extensive experience and emerging companies offering niche solutions. The market is expected to witness strategic partnerships, mergers and acquisitions, and new product launches in the coming years, further accelerating market consolidation and innovation. Growth will be particularly strong in regions with rapidly developing digital infrastructures and increasing government initiatives promoting biometric technologies.

In September 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a data collection as part of its Nail to Nail (N2N) Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail surface area of a fingerprint equivalent to a rolled fingerprint from an unacclimated user, without assistance from a trained operator. Traditional operator-assisted live-scan rolled fingerprints were also captured, along with assorted other friction ridge live-scan and latent captures. In this data collection, study participants needed to have their fingerprints captured using traditional operator-assisted techniques in order to quantify the performance of the Challenger devices. IARPA invited members of the Federal Bureau of Investigation (FBI) Biometric Training Team to the data collection to perform this task. Each study participant had N2N fingerprint images captured twice, each by a different FBI expert, resulting in two N2N baseline datasets. To ensure the veracity of recorded N2N finger positions in the baseline datasets, Challenge test staff also captured plain fingerprint impressions in a 4-4-2 slap configuration. This capture method refers to simultaneously imaging the index, middle, ring, and little fingers on the right hand, then repeating the process on the left hand, and finishing with the simultaneous capture of the left and right thumbs. This technique is a best practice to ensure finger sequence order, since it is physically challenging for a study participant to change the ordering of fingers when imaging them simultaneously. There were four baseline (two rolled and two slap), eight challenger and ten auxiliary fingerprint sensors deployed during the data collection, amassing a series of rolled and plain images. It was required that the baseline devices achieve 100% acquisition rate, in order to verify the recorded friction ridge generalized positions (FRGPs) and study participant identifiers for other devices. There were no such requirements for Challenger devices. Not all devices were able to achieve 100% acquisition rate. Plain, rolled, and touch-free impression fingerprints were captured from a multitude of devices, as well as sets of plain palm impressions. NIST also partnered with the FBI and Schwarz Forensic Enterprises (SFE) 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.

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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

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.

Biometric Recognition Market Outlook

The global biometric recognition market size is projected to grow from $25 billion in 2023 to $70 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12%. This impressive growth trajectory is driven by increasing security concerns and the widespread adoption of biometric technologies across various sectors. The need for enhanced security measures in both public and private sectors, coupled with the rising implementation of biometric systems in smartphones and other consumer electronics, is fueling this market's expansion.

One of the primary growth factors of the biometric recognition market is the escalating need for security in an increasingly digital world. With the proliferation of online transactions and the digitalization of services, there is a heightened need to protect sensitive information from unauthorized access and cyber threats. Biometric recognition systems offer a high level of security as they are difficult to forge or steal compared to traditional passwords or PINs. This has led to their growing adoption in sectors such as banking, financial services, and insurance (BFSI), healthcare, and government institutions.

Another significant driver is the technological advancements in biometric systems. Innovations such as artificial intelligence (AI) and machine learning (ML) are enhancing the accuracy and reliability of biometric recognition systems. These technologies enable the systems to learn and improve over time, reducing the chances of false positives and negatives. As a result, organizations are increasingly adopting these advanced systems to improve their security infrastructure. Additionally, the integration of biometric recognition with other technologies, such as Internet of Things (IoT) devices, is expanding the application areas of biometric systems, further propelling market growth.

The growing consumer electronics market, particularly smartphones and wearable devices, is also contributing to the increased adoption of biometric recognition technologies. Modern smartphones are equipped with fingerprint scanners, facial recognition systems, and even iris recognition features, making biometric authentication a standard feature in these devices. This widespread acceptance and usage of biometric systems in everyday consumer electronics are fostering a favorable market environment for biometric recognition technologies.

In the healthcare sector, the adoption of Healthcare Biometrics is revolutionizing patient identification and data security. By utilizing unique biological characteristics such as fingerprints, facial features, and iris patterns, healthcare providers can ensure accurate patient verification and secure access to medical records. This technology not only enhances patient safety by reducing errors in patient identification but also protects sensitive health information from unauthorized access. The integration of biometric systems in healthcare settings is becoming increasingly vital as the industry moves towards digital health records and telemedicine, providing a robust solution to the challenges of identity theft and data breaches.

Regionally, the Asia-Pacific region is expected to witness significant growth in the biometric recognition market. Countries like China, India, and Japan are investing heavily in biometric technologies for various applications, including national ID programs, border control, and financial transactions. The rapid digital transformation in these countries, combined with government initiatives to enhance security, is driving the adoption of biometric systems. Additionally, the increasing use of biometric recognition in the commercial sector, such as banking and retail, is further boosting the market in this region.

Technology Analysis

Fingerprint Recognition Analysis

Fingerprint recognition is one of the oldest and most widely used biometric technologies. Its reliability, ease of use, and relatively low cost have made it a popular choice for various applications, from unlocking smartphones to securing entry into buildings. The technology works by capturing the unique patterns of ridges and valleys on a person's fingerprint, which are then compared with stored fingerprint data for verification. The growth of the fingerprint recognition market is supported by the increasing demand for secure and convenient authentication methods in consumer electronics and access control systems.<

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.

The biometric database management services market is projected to reach a significant market size by 2033, exhibiting a robust compound annual growth rate (CAGR). The growth is driven by the increasing demand for secure and efficient identification and authentication solutions across industries. Key market drivers include rising concerns over data breaches, growing adoption of cloud-based services, and the proliferation of biometric technologies in various applications, including citizenship management, public security, medical file management, payment identity verification, and consumer electronics secure systems. Market segments include cloud-based and web-based services, with citizenship management, public security, consumer electronics secure systems, and medical file management being major application areas. Prominent companies operating in the market include Aware, Thales, NEC Corporation, Veridos, IDEMIA, Neurotechnology, M2SYS Technology, Innovatrics, Papillon Systems, and BioLink Solutions. The market is expected to witness significant growth opportunities across regions, including North America, South America, Europe, Middle East & Africa, and Asia Pacific, due to increasing investments in biometric technology and the growing adoption of digital identity management solutions in both the public and private sectors. The global biometric database management services market is projected to reach USD 4.5 billion by 2026, exhibiting a CAGR of 12.3% during the forecast period. The growing need for secure and efficient identity verification, coupled with the rising adoption of digital payment systems and government initiatives, is expected to drive market growth.

Overall, respondents in the United States were less confident that their biometric data was safeguarded and used properly by tech companies in 2024 compared to 2022. Only **** percent of respondents highly trusted the safe handling of their biometric data by tech firms, a significant drop from nearly ** percent in 2022.

The Automated Fingerprint Identification System (AFIS) market is projected to reach USD 16.47 billion by 2033, growing at a CAGR of 19.35%. The market is driven by increasing government initiatives to enhance public safety, rising demand for efficient and accurate identification systems in various sectors, and technological advancements leading to improved accuracy and efficiency of AFIS systems. Key trends in the market include the adoption of cloud-based AFIS solutions, integration of AI and machine learning algorithms to improve recognition accuracy, and increasing use of multi-modal biometrics for enhanced security. Key market players include NEC Corporation, Thales Group, Aware Inc., IDEMIA, and M2YSYS Technology. The market is expected to witness growth in all regions, with North America holding the largest market share. Growing concerns over identity theft and fraud, coupled with government regulations and initiatives, are expected to drive market growth in the coming years. Recent developments include: April 2023: The national database authority of Pakistan has introduced the impressively accurate automatic fingerprint identification system (AFIS), which was locally designed. Software developers from the National Database & Registration Authority created the system, which they have named "Nadir" (the Urdu word for "unique"). The automated system is expected to revolutionize how biometric identification is used in a variety of civil applications, including immigration, border control, e-governance, and social services. NADIR has attained an impressive accuracy rate above 99.5% based on the internationally renowned Fingerprint Verification Competition (FVC), a benchmarking exercise conducted in Italy. As a public service, NADRA can now reliably and swiftly store fingerprints & identify people for a variety of uses., According to the statement, NADRA's status as a pioneer in cutting-edge identity management solutions has been cemented with the introduction of the system. As a result of this breakthrough, Pakistan has now joined some countries who have developed their original AFIS technology and is prepared to sell its product on the global market to assist other nations in improving their identification procedures. The worldwide market for biometric identification technology, according to authority authorities, is mostly dominated by suppliers from industrialized nations including the US, Russia, Japan, the UK, France, Germany, & others., Ijazat Aap Ki, a latest data protection service that guarantees the security and privacy of citizen data, was introduced by NADRA in March. The first data protection service in Pakistan sends a 6-digit passcode to individuals' phones whenever a request is made for a transaction or personal information, giving users more control over their data and limiting unlawful access to the service providers, including banks., March 2022: Suprema launched a new generation of credentials control products that concentrates heavily on contactless solutions. The star of Suprema's production was BioStation 3, an all-in-one access control solution that delivers numerous credential options, from facial recognition and mobile access to barcodes, QR codes, and RFID cards. BioStation 3 will be emitted in the second half of the year., November 2021: Suprema announced the successful integration of its biometric access control products with Genetec Security Center, a unified security platform that connects security systems, sensors, and data in a single intuitive interface.. Notable trends are: Growing government initiatives are driving the market growth.

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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 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.

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...

The dataset provides a collection of behaviour biometrics data (commonly known as Keyboard, Mouse and Touchscreen (KMT) dynamics). The data was collected for use in a FinTech research project undertaken by academics and researchers at Computer Science Department, Edge Hill University, United Kingdom. The project called CyberSIgnature uses KMT dynamics data to distinguish between legitimate card owners and fraudsters. An application was developed that has a graphical user interface (GUI) similar to a standard online card payment form including fields for card type, name, card number, card verification code (cvc) and expiry date. Then, user KMT dynamics were captured while they entered fictitious card information on the GUI application.

The dataset consists of 1,760 KMT dynamic instances collected over 88 user sessions on the GUI application. Each user session involves 20 iterations of data entry in which the user is assigned a fictitious card information (drawn at random from a pool) to enter 10 times and subsequently presented with 10 additional card information, each to be entered once. The 10 additional card information is drawn from a pool that has been assigned or to be assigned to other users. A KMT data instance is collected during each data entry iteration. Thus, a total of 20 KMT data instances (i.e., 10 legitimate and 10 illegitimate) was collected during each user entry session on the GUI application.

The raw dataset is stored in .json format within 88 separate files. The root folder named behaviour_biometrics_dataset' consists of two sub-foldersraw_kmt_dataset' and `feature_kmt_dataset'; and a Jupyter notebook file (kmt_feature_classificatio.ipynb). Their folder and file content is described below:

-- raw_kmt_dataset': this folder contains 88 files, each namedraw_kmt_user_n.json', where n is a number from 0001 to 0088. Each file contains 20 instances of KMT dynamics data corresponding to a given fictitious card; and the data instances are equally split between legitimate (n = 10) and illegitimate (n = 10) classes. The legitimate class corresponds to KMT dynamics captured from the user that is assigned to the card detail; while the illegitimate class corresponds to KMT dynamics data collected from other users entering the same card detail.

-- feature_kmt_dataset': this folder contains two sub-folders, namely:feature_kmt_json' and feature_kmt_xlsx'. Each folder contains 88 files (of the relevant format: .json or .xlsx) , each namedfeature_kmt_user_n', where n is a number from 0001 to 0088. Each file contains 20 instances of features extracted from the corresponding `raw_kmt_user_n' file including the class labels (legitimate = 1 or illegitimate = 0).

-- `kmt_feature_classification.ipynb': this file contains python code necessary to generate features from the raw KMT files and apply simple machine learning classification task to generate results. The code is designed to run with minimal effort from the user.

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.

Sumaiya Ahmad