4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.

According to Cognitive Market Research, the global Facial Recognition market will be USD 6515.2 million in 2024 and expand at a compound annual growth rate (CAGR) of 17.0% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 2606.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 1954.56 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 1498.50 million in 2024 and will grow at a compound annual growth rate (CAGR) of 19.0% from 2024 to 2031.
Latin America's market has more than 5% of the global revenue, with a market size of USD 325.76 million in 2024, and will grow at a compound annual growth rate (CAGR) of 16.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 130.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.7% from 2024 to 2031.
The government and defense held the highest facial recognition market revenue share in 2024.

Market Dynamics of Facial Recognition Market

Key Drivers of Facial Recognition Market

Advancements in Technology to Increase the Demand Globally

More advancements in 3D facial recognition and enhanced algorithms make identity recognition more accurate. This increases the technology's dependability for other uses, such as security. The availability of facial recognition software is growing as a cloud-based service. This lowers the barrier to technology adoption for enterprises by removing the need for costly hardware and infrastructure purchases. Artificial intelligence (AI) developments enable facial recognition systems to perform functions beyond simple identification. They can now assess demographics and facial expressions, opening up new possibilities for customer service, marketing, and other fields. The market is expanding because of the increased range of applications for facial recognition that these developments are enabling.

Furthermore, the precision offered by 3D facial recognition systems motivates using these systems for public safety applications, including surveillance and border protection. 3D recognition systems better serve high-security areas such as airports than 2D ones. All of these factors will strengthen the worldwide market.

Increasing Security Concerns to Propel Market Growth

As security concerns grow, facial recognition technology is increasingly employed. This is a key element driving the market for facial recognition technology's growth. People in busy places like train stations, airports, and city centers can be recognized and followed using facial recognition technology. Terrorist acts and criminal activity can both be prevented by this. Travelers' identities can be confirmed via facial recognition, as can the identities of those on watchlists. By doing this, illegal immigration can be stopped, and border security can be strengthened. When someone uses an ATM or other financial facility, facial recognition technology can be used to confirm their identification. Fraud and identity theft may be lessened, and facial recognition can control access to buildings and other secure areas. This can help to prevent unauthorized access and protect sensitive information.

Restraint Factors Of Facial Recognition Marke

Privacy Concerns and Technical Limitations to Limit the Sales

One major obstacle to the widespread application of facial recognition technology is privacy concerns, including the possibility of governments or law enforcement abusing face recognition data. Hacking of facial recognition data could lead to identity theft or unauthorized access to personal data. There is a possibility for widespread monitoring and tracking of individuals without their knowledge or agreement through mass surveillance. The use of facial recognition technology is now subject to certain laws and limitations as a result of privacy concerns. For instance, the General Data Protection Regulation (GDPR) in Europe imposes stringent restrictions on the collection and use of face recognition data, and several American towns have outlawed the use of facial recognition technology by law enforcement. The future of the facial recognition market is unclear. ...

3D Face Recognition System Market Outlook

The global market size for 3D face recognition systems was valued at approximately USD 3.5 billion in 2023 and is expected to soar to an estimated USD 12.8 billion by 2032, registering a remarkable CAGR of 15.3% during the forecast period. This impressive growth is driven by a confluence of factors including technological advancements, increased demand for enhanced security, and widespread adoption across various industries.

One of the primary growth factors for the 3D face recognition system market is the rising need for advanced security solutions. With the increasing number of security breaches and identity frauds, organizations are investing heavily in robust security systems. 3D face recognition technology offers superior accuracy and reliability compared to traditional 2D systems, making it an attractive option for various sectors such as BFSI, government, and healthcare. Additionally, advancements in artificial intelligence and machine learning have significantly improved the performance and efficiency of these systems, further driving their adoption.

Another significant factor contributing to the market's growth is the proliferation of smart devices and the Internet of Things (IoT). The integration of 3D face recognition technology in smartphones, tablets, and other smart devices has made it more accessible to consumers. This widespread adoption is not just limited to personal devices but extends to commercial and industrial applications such as access control, attendance tracking, and surveillance systems. The increasing trend of smart cities and connected infrastructure also provides a fertile ground for the expansion of the 3D face recognition system market.

The healthcare sector is also emerging as a lucrative market for 3D face recognition systems. With the advent of telemedicine and remote patient monitoring, there is a growing need for reliable and secure patient identification methods. 3D face recognition technology offers a non-intrusive and efficient solution for patient identification and verification, thereby streamlining healthcare services and enhancing patient experience. Moreover, the ongoing COVID-19 pandemic has further accelerated the adoption of contactless technologies, including 3D face recognition, to minimize the risk of virus transmission.

From a regional perspective, North America holds the largest market share due to the early adoption of advanced technologies and the presence of key market players. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, government initiatives for smart city projects, and increasing investments in security infrastructure. Europe also presents significant growth opportunities, particularly in sectors such as retail, banking, and government services.

Facial Authentication Systems have become an integral part of modern security solutions, offering a seamless and secure method for identity verification. As organizations strive to enhance their security measures, facial authentication systems provide a reliable alternative to traditional methods such as passwords or PINs. These systems leverage advanced algorithms and 3D imaging techniques to accurately capture and analyze facial features, ensuring high levels of accuracy and security. The growing demand for contactless authentication, particularly in the wake of the COVID-19 pandemic, has further accelerated the adoption of facial authentication systems across various sectors, including banking, healthcare, and government services. This trend is expected to continue as the technology evolves, offering even more sophisticated and efficient solutions for identity verification.

Component Analysis

The 3D face recognition system market is segmented into hardware, software, and services. The hardware segment includes various components such as sensors, cameras, and processors essential for capturing and processing 3D facial data. This segment is expected to witness substantial growth, driven by continuous advancements in sensor technology and increasing demand for high-quality, reliable hardware components. Companies are investing heavily in R&D to develop innovative sensors that can capture more accurate and detailed facial data, thereby enhancing the overall performance of face recognition systems.

The software segment encompasses a wide range of solutions, in

The Facial Recognition Market is Segmented by Technology (2D Facial Recognition, 3D Facial Recognition, and Facial Analytics), Application (Access Control, Security and Surveillance, and Other Applications), End-User (Security and Law Enforcement, Healthcare, Retail and E-Commerce, BFSI, Automobile, and Transportation, Telecom and IT, Media and Entertainment, and Other End-Users), and Geography (North America, Europe, Asia-Pacific, Latin America, and Middle East and Africa). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

Facial Recognition Market size was valued at USD 6.15 Billion in 2024 and is projected to reach USD 14.62 Billion by 2031, growing at a CAGR of 12.62% from 2024 to 2031.

Facial Recognition Market Drivers

Security and Surveillance: Facial recognition is used for security and surveillance purposes, such as access control, law enforcement, and crowd management.
Payment and Authentication: Facial recognition is being adopted for biometric authentication, enabling secure payments and logins.
Consumer Electronics: Facial recognition is integrated into smartphones, laptops, and other devices for unlocking, authentication, and facial recognition apps.

Facial Recognition Market Restraints

Privacy Concerns: The use of facial recognition raises privacy concerns, as it involves collecting and storing biometric data.
Accuracy and Bias: Facial recognition systems may not be accurate for all individuals, especially those with darker skin tones or facial features that are not well-represented in training data.
Regulatory Challenges: Governments are implementing regulations to address privacy concerns and ensure ethical use of facial recognition technology.

Market Overview:

United States facial recognition market size is projected to exhibit a growth rate (CAGR) of 14.50% during 2024-2032. The growing utilization of enhanced security solutions for identity verification during online transactions, reducing fraud, and improving online security, rising focus on hygiene and contactless interactions, and increasing need to enhance public safety represent some of the key factors driving the market.

Facial recognition is an advanced technology that is used to identify and verify individuals by analyzing unique facial features. It works by capturing and analyzing various facial characteristics, such as the shape of the face, eyes, nose, and mouth, and even specific details like facial landmarks and skin texture. It can help identify suspects captured on surveillance cameras and locate missing persons. It plays a crucial role in places that require precise identification of individuals. It offers a quick and accurate means of authentication, significantly enhancing security and convenience. It allows organizations to personalize marketing efforts and streamline payment processes. It assists in reducing the risk of unauthorized access to secure areas or devices. Besides this, it aids in streamlining verification processes and saves time. As it is beneficial in preventing identity theft and fraud, the demand for facial recognition is rising in the United States.

United States Facial Recognition Market Trends:

The escalating demand for facial recognition in smartphones and tablets to allow users to unlock their devices, access apps, and make secure transactions represents one of the major factors influencing the market positively in the United States. Additionally, the increasing employment of facial recognition in healthcare settings, as it helps with patient identification and access control and improves the efficiency and security of healthcare services, is impelling the growth of the market in the country. Apart from this, there is a rise in the need for facial recognition to enhance public safety. This, coupled with the growing utilization of facial recognition in access control systems to grant or deny entry to secure areas, is offering a positive market outlook in the country. Moreover, the increasing application of facial recognition in e-commerce platforms for identity verification during online transactions, reducing fraud, and improving online security is bolstering the growth of the market. In line with this, advancements in artificial intelligence (AI) and machine learning (ML) algorithms are improving the accuracy and performance of facial recognition systems, which is strengthening the market growth in the US. Furthermore, the rising focus on hygiene and contactless interactions among the masses is providing lucrative growth opportunities to industry investors. In addition, the integration of facial recognition with other biometric technologies, such as fingerprint scanning and iris recognition, to enhance overall security measures is contributing to the market growth in the country. The increasing adoption of facial recognition in organizations to comply with industry-specific regulations and security standards is propelling the market growth.

United States Facial Recognition Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, technology, application, and end use industry.

Component Insights:

https://www.imarcgroup.com/CKEditor/a73d0bc1-d9f7-4af2-8536-738945183f3dunited-states-facial-recognition-market-sagment.webp" style="height:450px; width:800px" />

• Software
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes software and services.

Technology Insights:

• 2D Facial Recognition
• 3D Facial Recognition
• Facial Analytics
  

A detailed breakup and analysis of the market based on the technology have also been provided in the report. This includes 2d facial recognition, 3d facial recognition, and facial analytics.

Application Insights:

• Emotion Recognition
• Attendance Tracking and Monitoring
• Access Control
• Security and Surveillance
• Others
  

The report has provided a detailed breakup and analysis of the market based on the application. This includes emotion recognition, attendance tracking and monitoring, access control, security and surveillance, and others.

End Use Industry Insights:

• Retail and E-commerce
• BFSI
• Government and Defense
• Automotive and Transportation
• Media and Entertainment
• Healthcare
• Telecom and IT
• Others
  

A detailed breakup and analysis of the market based on the end use industry have also been provided in the report. This includes retail and e-commerce, BFSI, government and defense, automotive and transportation, media and entertainment, healthcare, telecom and IT, and others.

Regional Insights:

https://www.imarcgroup.com/CKEditor/fbfbe3d1-3ff3-4bda-8939-56b436c0454cunited-states-facial-recognition-market-regional.webp" style="height:450px; width:800px" />

• Northeast
• Midwest
• South
• West
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northeast, Midwest, South, and West.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

United States Facial Recognition Market Report Coverage:

The global 3D face swiping payment device market is experiencing robust growth, projected to reach $5.486 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.4% from 2025 to 2033. This expansion is fueled by several key factors. Increased consumer demand for contactless payment solutions, driven by hygiene concerns and the convenience of biometric authentication, is a primary driver. The rising adoption of digital payment methods globally, coupled with advancements in facial recognition technology offering enhanced security and accuracy, further contributes to market growth. Moreover, the increasing integration of 3D face swiping technology into various sectors like retail (shops), hospitality (restaurants), and finance (banks) expands the market's addressable audience. While data privacy concerns and the initial investment costs for businesses adopting this technology present some restraints, the overall market trajectory points towards significant expansion. The market segmentation by application (shop, restaurant, bank, others) and type (desktop, floor-standing) reveals diverse application possibilities and evolving hardware solutions catering to various business needs and space constraints. Leading players like Mastercard, PopID, and NEC Corporation are actively driving innovation and market penetration through strategic partnerships and technological advancements. Geographical expansion is another notable trend, with North America and Asia Pacific anticipated to be key contributors to market growth due to their advanced technological infrastructure and high adoption rates of digital payments. The market's future growth will depend on several factors including the continued refinement of facial recognition technology to address security and privacy concerns, the decreasing cost of implementation, and the expansion of its applications into new sectors. Government regulations surrounding data privacy and biometric authentication will also play a crucial role in shaping the market landscape. Furthermore, the success of 3D face swiping payment devices will hinge on consumer acceptance and trust in the technology's security and reliability. Continued investment in research and development by major players will be crucial in overcoming technical limitations and broadening the adoption of this innovative payment method. The market's continued strong performance is expected, given these positive factors and the overall trend towards contactless and biometric payment solutions.

The size and share of the market is categorized based on Type (2D Face Recognition, 3D Face Recognition) and Application (SMEs, Large Enterprises) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

Global 3D Facial Recognition Systems market size 2025 was XX Million. 3D Facial Recognition Systems Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

The 3D face swiping payment device market is experiencing robust growth, projected to reach a market size of $5.486 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12.4% from 2019 to 2033. This expansion is driven by several key factors. Firstly, the increasing adoption of contactless payment methods, fueled by concerns over hygiene and the convenience they offer, is significantly boosting market demand. Secondly, advancements in facial recognition technology, leading to more secure and accurate authentication processes, are contributing to wider acceptance. Thirdly, the growing integration of 3D face swiping technology into existing point-of-sale (POS) systems across various sectors, including retail shops, restaurants, and banking institutions, is expanding market penetration. The market is segmented by application (Shop, Restaurant, Bank, Others) and device type (Desktop, Floor-standing), allowing for tailored solutions and targeted market strategies. Major players like Mastercard, PopID, and NEC Corporation are driving innovation and competition, pushing the boundaries of biometric payment security and convenience. While initial investment costs and potential data security concerns could act as restraints, the overall market outlook remains positive, projecting substantial growth throughout the forecast period. The geographical distribution of this market shows significant potential across various regions. North America and Europe currently hold a substantial market share, driven by early adoption of advanced payment technologies and strong regulatory frameworks. However, Asia Pacific, particularly China and India, are expected to witness the fastest growth rates in the coming years due to increasing smartphone penetration, burgeoning e-commerce sectors, and a growing young population comfortable with digital payment solutions. The increasing governmental initiatives focused on digital financial inclusion in developing economies also contributes to the expansion in these regions. Furthermore, the ongoing development and refinement of the underlying facial recognition technology will continue to enhance user experience, security, and overall market acceptance, contributing to continued market expansion and a promising future for the 3D face swiping payment device market.

The US Facial Recognition Market Report is Segmented By Technology (3D Facial Recognition, 2D Facial Recognition, Facial Analytics), and End User.

The global face recognition solution market size is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market growth is primarily driven by increasing concerns regarding security and public safety, along with technological advancements in facial recognition algorithms. Moreover, the adoption of face recognition systems in various applications, such as law enforcement, border security, and surveillance, is further fueling market growth. Key market trends include the growing adoption of AI and machine learning (ML) algorithms, the integration of face recognition systems with other security solutions, and the increasing use of mobile-based face recognition systems. Furthermore, the market is segmented based on application, type, and region. By application, the market is divided into emotion recognition, law enforcement, surveillance and monitoring, and others. By type, the market is classified into 2D face recognition and 3D face recognition. Regionally, the market is analyzed across North America, South America, Europe, the Middle East & Africa, and Asia Pacific. North America is expected to hold a significant market share due to the early adoption of face recognition technologies and the presence of key industry players. However, Asia Pacific is anticipated to witness the highest growth rate during the forecast period, owing to increasing government initiatives and investments in public safety and infrastructure development.

This report provides a detailed analysis of the face recognition solution market, with particular attention to the impact of research and development and innovation on the overall industry. The report also provides a comprehensive overview of the market, including market size, segmentation, regional analysis, competitive landscape, and key trends.

The 3D Mask Attack Database (3DMAD) is a biometric (face) spoofing database. It contains 76500 frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks. Each frame consists of:

• a depth image (640x480 pixels – 1x11 bits)
• the corresponding RGB image (640x480 pixels – 3x8 bits)
• manually annotated eye positions (with respect to the RGB image).

The data is collected in 3 different sessions for all subjects and for each session 5 videos of 300 frames are captured. The recordings are done under controlled conditions, with frontal-view and neutral expression. The first two sessions are dedicated to the real access samples, in which subjects are recorded with a time delay of ~2 weeks between the acquisitions. In the third session, 3D mask attacks are captured by a single operator (attacker).

In each video, the eye-positions are manually labelled for every 1st, 61st, 121st, 181st, 241st and 300th frames and they are linearly interpolated for the rest.

The real-size masks are obtained using "ThatsMyFace.com". The database additionally contains the face images used to generate these masks (1 frontal and 2 profiles) and paper-cut masks that are also produced by the same service and using the same images.

The satellite package which contains the Bob accessor methods to use this database directly from Python, with the certified protocols, is available in two different distribution formats:

1. You can download it from PyPI, or
2. You can download it in its source form from its git repository.

Acknowledgments

If you use this database, please cite the following publication:

Nesli Erdogmus and Sébastien Marcel, "Spoofing in 2D Face Recognition with 3D Masks and Anti-spoofing with Kinect", Biometrics: Theory, Applications and Systems, 2013.
10.1109/BTAS.2013.6712688
https://publications.idiap.ch/index.php/publications/show/2657

The facial recognition access control system market has experienced significant growth in recent years, driven by advancements in technology and increasing demand for enhanced security measures. The market is estimated to reach a value of XXX million by 2033, exhibiting a CAGR of XX% during the forecast period 2025-2033. This growth is attributed to increasing concerns over security threats, the need for efficient and contactless access control solutions, and advancements in facial recognition algorithms. The market for facial recognition access control systems is segmented based on application, type, and region. By application, the market is divided into residential, non-financial enterprises, and others. The non-financial enterprises segment holds the largest market share due to the increasing adoption of facial recognition systems in commercial buildings, offices, and other workplaces. By type, the market is segmented into 3D face recognition and iris recognition. The 3D face recognition segment is expected to grow at a faster pace during the forecast period due to its higher accuracy and security level. Regionally, the Asia Pacific region is anticipated to dominate the market, followed by North America and Europe, which are expected to experience growth due to increasing urbanization and technological advancements.

The size and share of the market is categorized based on Application (Access Control, Attendance Tracking And Monitoring, Law Enforcement, Others) and Product (Hardware, Software) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The ChokePoint dataset is designed for experiments in person identification/verification under real-world surveillance conditions using existing technologies. An array of three cameras was placed above several portals (natural choke points in terms of pedestrian traffic) to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images (ie. a face set) can be captured. Faces in such sets will have variations in terms of illumination conditions, pose, sharpness, as well as misalignment due to automatic face localisation/detection. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal.

The dataset consists of 25 subjects (19 male and 6 female) in portal 1 and 29 subjects (23 male and 6 female) in portal 2. The recording of portal 1 and portal 2 are one month apart. The dataset has frame rate of 30 fps and the image resolution is 800X600 pixels. In total, the dataset consists of 48 video sequences and 64,204 face images. In all sequences, only one subject is presented in the image at a time. The first 100 frames of each sequence are for background modelling where no foreground objects were presented.

Each sequence was named according to the recording conditions (eg. P2E_S1_C3) where P, S, and C stand for portal, sequence and camera, respectively. E and L indicate subjects either entering or leaving the portal. The numbers indicate the respective portal, sequence and camera label. For example, P2L_S1_C3 indicates that the recording was done in Portal 2, with people leaving the portal, and captured by camera 3 in the first recorded sequence.

To pose a more challenging real-world surveillance problems, two seqeunces (P2E_S5 and P2L_S5) were recorded with crowded scenario. In additional to the aforementioned variations, the sequences were presented with continuous occlusion. This phenomenon presents challenges in identidy tracking and face verification.

This dataset can be applied, but not limited, to the following research areas:

person re-identification

image set matching

face quality measurement

face clustering

3D face reconstruction

pedestrian/face tracking

background estimation and subtraction

Please cite the following paper if you use the ChokePoint dataset in your work (papers, articles, reports, books, software, etc):

Y. Wong, S. Chen, S. Mau, C. Sanderson, B.C. Lovell Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, pages 81-88, 2011. http://doi.org/10.1109/CVPRW.2011.5981881

North America Facial Recognition Market will be USD 2606.08 million in 2024 and expand at a compound annual growth rate (CAGR) of 15.2% from 2024 to 2031.

The Custom Silicone Mask Attack Dataset (CSMAD) contains presentation attacks made of six custom-made silicone masks. Each mask cost about USD 4000. The dataset is designed for face presentation attack detection experiments.

The Custom Silicone Mask Attack Dataset (CSMAD) has been collected at the Idiap Research Institute. It is intended for face presentation attack detection experiments, where the presentation attacks have been mounted using a custom-made silicone mask of the person (or identity) being attacked.

The dataset contains videos of face-presentations, as a set of files specifying the experimental protocol corresponding the experiments presented in the corresponding publication.

Reference

If you publish results using this dataset, please cite the following publication.

Sushil Bhattacharjee, Amir Mohammadi and Sebastien Marcel: "Spoofing Deep Face Recognition With Custom Silicone Masks." in Proceedings of International Conference on Biometrics: Theory, Applications, and Systems (BTAS), 2018.
10.1109/BTAS.2018.8698550
http://publications.idiap.ch/index.php/publications/show/3887

Data Collection

Face-biometric data has been collected from 14 subjects to create this dataset. Subjects participating in this data-collection have played three roles: targets, attackers, and bona-fide clients. The subjects represented in the dataset are referred to here with letter-codes: A .. N. The subjects A..F have also been targets. That is, face-data for these six subjects has been used to construct their corresponding flexible masks (made of silicone). These masks have been made by Nimba Creations Ltd., a special effects company.

Bona fide presentations have been recorded for all subjects A..N. Attack presentations (presentations where the subject wears one of 6 masks) have been recorded for all six targets, made by different subjects. That is, each target has been attacked several times, each time by a different attacker wearing the mask in question. This is one way of increasing the variability in the dataset. Another way we have augmented the variability of the dataset is by capturing presentations under different illumination conditions. Presentations have been captured in four different lighting conditions:

• flourescent ceiling light only
• halogen lamp illuminating from the left of the subject only
• halogen lamp illuminating from the right only
• both halogen lamps illuminating from both sides simultaneously

All presentations have been captured with a green uniform background. See the paper mentioned above for more details of the data-collection process.

Dataset Structure

The dataset is organized in three subdirectories: ‘attack’, ‘bonafide’, ‘protocols’. The two directories: ‘attack’ and ‘bonafide’ contain presentation-videos and still images for attacks and bona fide presentations, respectively. The folder ‘protocols’ contains text files specifying the experimental protocol for vulnerability analysis of face-recognition (FR) systems.

The number of data-files per category are as follows:

• ‘bonafide’: 87 videos, and 17 still images (in .JPG format). The still images are frontal face images captured using a Nikon Coolpix digital camera.
• ‘attack’: 159, organized in two sub-folders – ‘WEAR’ (108 videos), and ‘STAND’ (51 videos)

The folder ‘attack/WEAR’ contains videos where the attack has been made by a person (attacker) wearing the mask of the target being attacked. The ‘attack/STAND’ folder contains videos where the attack has been made using a the target’s mask mounted on an appropriate stand.

Video File Format

The video files for the face-presentations are in ‘hdf5’ format (with file-extensions ‘.h5’. The folder structure of the hdf5 file is shown in Figure 1. Each file contains data collected using two cameras:

• RealSense SR300 (from Intel): collects images/videos in visible-light (RGB color) , near infrared (NIR) @ 860nm wavelength, and depth maps
• Compact Pro (from Seek Thermal): collects thermal (long-wave infrared (LWIR)) images.

As shown in Figure 1, frames from the different channels (color, infrared, depth, thermal) from he two cameras are stored in separate directory-hierarchies in the hdf5 file. Each file respresents a video of approximately 10 seconds, or roughly, 300 frames.

In the hdf5 file, the directory for SR300 also contains a subdirectory named ‘aligned_color_to_depth’. This folder contains post-processed data, where the frames of depth channel have been aligned with those of the color channel based on the time-stamps of the frames.

Experimental Protocol

The ‘protocols’ folder contains text files that specify the protocols for vulnerability analysis experiments reported in the paper mentioned above. Please see the README file in the protocols folder for details.

El tamaño del mercado del mercado de tecnología de reconocimiento facial se clasifica en función de tipo (reconocimiento facial 2D, reconocimiento facial 3D, reconocimiento facial térmico) y Aplicación (Reconocimiento de emociones, aplicación de la ley, Vigilancia y monitoreo, otros) y regiones geográficas (América del Norte, Europa, Asia-Pacífico, América del Sur y Medio Oriente y África).

Este informe proporciona información sobre el tamaño del mercado y pronostica el valor de El mercado, expresado en millones de dólares, en estos segmentos definidos.

1,417 People – 3D Living_Face & Anti_Spoofing Data. The collection scenes include indoor and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes various expressions, facial postures, anti-spoofing samples, multiple light conditions, multiple scenes. This data can be used for tasks such as 3D face recognition, 3D Living_Face & Anti_Spoofing.