We are turning bearish and have confluence across multiple timeframes and systems
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Remark Holdings, Inc. (Nasdaq: MARK) is a leading provider of artificial intelligence (AI)-powered video analytics solutions, with headquarters in Las Vegas. The company specializes in leveraging cutting-edge AI technologies to enhance safety, security, and operational efficiency across various industries. Remark's innovative solutions are designed to analyze real-time video content, providing actionable insights that contribute to the protection of cities, employees, public spaces, and valuable assets.
The company has garnered attention for its Smart Safety Platform (SSP), a robust AI-driven system that rapidly analyzes video content at scale. This platform offers a range of features, including crowd density analysis, behavioral analytics, and anomaly detection within public spaces. What sets Remark apart is its ability to deliver real-time alerts, providing a proactive approach to security that can prevent incidents before they escalate. The integration of Remark AI's models into existing video infrastructure enhances the accuracy and speed of identifying assets, objects, and people.
Remark Holdings has strategically positioned itself in the market through key collaborations and partnerships. Notably, the company has initiated a sales and marketing collaboration with Arrow Electronics and Intel. This collaboration aims to expand Remark's market reach to Arrow's extensive customer base, providing Intel-powered AI servers that run the SSP. Arrow Electronics, in turn, supports the initiative through distribution, inventory management, and logistical sales support. Such collaborations underscore Remark's commitment to scaling its AI video analytics solutions across diverse sectors.
The company's industry recognition is highlighted by its acknowledgment as a representative vendor for Computer Vision Perceptive Systems by Gartner AI Analyst Erick Brethenoux. This recognition adds a layer of credibility to Remark's AI capabilities, showcasing its standing in the field of artificial intelligence. Erick Brethenoux's expertise in AI techniques and decision intelligence further emphasizes the company's commitment to technological innovation and operationalizing AI for growth.
Remark Holdings actively participates in major industry events, exemplified by its presence at the Gartner IT Symposium/XPO conference. These events provide Remark with a platform to engage with industry experts, showcase its AI-driven solutions, and explore collaborative opportunities. Beyond security applications, Remark's focus on practical solutions for waste management, as mentioned in the article, demonstrates its commitment to leveraging AI for environmental sustainability. Overall, Remark Holdings stands at the forefront of AI innovation, bridging the gap between technology and practical solutions that positively impact both businesses and communities.
Efficient face recognition swiftly identifies individuals in real-time using digital imagery sourced from videos, portable device cameras, and external image files. Its applications span various aspects of daily life, encompassing tasks such as check-in, registration, payment, access control, security alerts, and police criminal investigations. Despite its widespread use, the accuracy of face recognition involves intricate engineering, influenced by factors like camera installation, resolution, video quality, lighting, image quality, camera type, and model parameters.
Remark Holdings, Inc. (NASDAQ: MARK) has high-potency facial products, their performance across diverse hardware platforms, software features for optimal functionality, and real-world use cases across multiple business applications. Key advantages of the Remark Vision SDK include top-ranking in Face Recognition and Tracking by NIST, GDPR certification ensuring adherence to strict privacy standards, robust security for data at rest and in motion, stellar performance in both speed and accuracy, a highly responsive design, rapid application development with low-code, no-code trainable models, an extensible and scalable microservices-based architecture, and versatile multi-platform support for Android, iOS, Windows, Linux (ARM), and Linux (x86).
Market Objective & Challenge
The primary business applications for face recognition revolve around 1:1 ID verification and 1:N ID matching. These mechanisms operate in two distinct environments - the "Prepared scenario" involves individuals consciously presenting their faces in controlled situations like kiosk check-ins or mobile registrations.
Conversely, the "Wild scenario" captures faces incidentally as individuals pass by and are unconsciously recorded by CCTV cameras. Access control, a key facet of face recognition, entails deciding whether to grant entry (access) to an individual in a specified area. In 1:1 face recognition for access control, each face is compared to a single image in a controlled setting, ensuring a high accuracy rate. This is often observed in scenarios like airport passport control, where a person's passport image is matched with a real-time scan. On the other hand, "In the wild" scenarios involve 1:N face recognition in non-controlled environments, where each face is compared to numerous faces in a dataset. This approach proves effective in identifying criminals or public offenders in diverse and uncontrolled settings.
Images may come from different angles, focal lengths of cameras, conditions, day and night, and indoor/outdoor environments. To achieve the best accuracy for face recognition, cameras should ideally be placed at eye level, with good lighting, and people should walk and look directly into the camera. However, for most of the scenarios that clients encounter, situations are much more complex, and cameras are not always positioned and configured in an ideal way for face recognition. We must figure out how to balance the variables to achieve the best possible results for these scenarios.
Working Pipeline Flow
The picture capture collects raw image data before feeding it into Remark Vision's face model pipeline. The capture section determines the prior quality of the whole process and final performance. Moreover, as the primary quality factor, they can hardly be modified or corrected by the post-processing algorithm within the pipeline. As a result, it plays a vital role in deployment before using in the actual application.
The type of cameras that are installed affect the performance of face recognition sufficiently. Surveillance (CCTV) cameras generally produce smaller and low-quality face images, resulting in poorer results for face recognition. Fish-eye/multi-sensor stitching cameras, combining multiple images from different sensors to provide 180 or 360 degree views of the scene, are designed for particular user scenarios and are not optimal for face recognition. Thermal cameras (infrared channels), which are helpful for temperature measurement and sight in poorly lit areas or at night, cannot be used for identity recognition. It is recommended that color cameras be used wherever possible.
For face recognition, the camera shall ideally be placed in all entrances and exits with a vertical angle of approximately 45 degrees or more so that the occlusions are minimal. In addition, when possible, it's best to have the scene set up where the people are walking in and out individually and not in groups, such as through a turn style. In general, identity recognition can generate optimal performance under the circumstance below:
• The cameras are positioned at eye level.
• The lighting is sufficient, resulting in a quick shutter speed that produces a crisp image.
• The lighting gives good contrast, but the faces are not lit from behind.
• The camera focus on the area where you expect faces to appear.
• The camera is steady to prevent image smearing.
Framerate Per Second
Framerate Per Second (FPS) is how many images a camera produces consecutively during a second. Our AI models can work with high or low framerate, but higher than 8 FPS is recommended. A lower framerate may miss faces passing by quickly or faces of best angle timing.
Speed of Moving Object
The speed that a person is traveling affects the accuracy of face recognition. This is because the person may become blurred as the speed increases due to how cameras capture the image. Another factor is that when an object travels faster, it appears in fewer frames, affecting accuracy.
The bitrate is the data size that a camera generates during a second. Bitrate is directly related to pixels per frame (resolution), FPS, color depth, and encoding type, together as a combination. Bitrate can be used to control the video quality as an overall parameter as higher bitrate results in better quality. On the other hand, low bitrates save storage and transmission bandwidth. Two video streams of the exact resolution can be configured to a different bitrate, where the higher the bitrate, the higher the quality, given that other parameters are identical. Because the bitrate determines the quality of the video, the bitrate affects the performance of identity recognition. The better the quality of the video and images results in highly accurate the better identity recognition as more object features will be extracted accurately. Higher bitrates are necessary for more challenging scenes, such as partial occlusions or dark backgrounds, to achieve successful identity recognition. When the camera's field of view is wide, and objects look smaller, a higher resolution and bitrate are required for face recognition accuracy.
All Things Considered, They've Got It Taken Care Of
Quality Requirement & Modification
The cropped object image by Remark AI face detection model stage is processed into the object image quality stage. Identity or object quality indicators covering a wide range of dimensions are produced. On the contrary to the quality in the picture capture section as prior factors, indicators in this section are posterior output by models. Several modification processes are conducted to improve the condition. Moreover, various settings for these quality metrics in the SSP AI function manager can be adjusted by users in their business applications.
Real-world face pose for capture is recommended as yaw≤10°, pitch≤10°, roll≤10° The quality model also calculates identity/Object pose metrics as model object/identity pose. Object alignment is tried to get the best front face layout, e.g., rotation of the cropped object image to minimize the roll angle, but not perfect font object can always be restored for the pitch and yaw angle. Such model object pose is compared to the pre-set pose criteria to check whether processing the next section of face recognition or not. The user can adjust the criteria to optimize the performance.
Image Metrics: Size, Brightness, Contrast
Image quality metrics are also evaluated by the quality model and are compared with the pre-set criteria. Several modifications are conducted to adjust the image to boost the quality by algorithm, but it can only improve the situation to some degree.
Moreover, tolerance thresholds can be set up in the AI function manager to determine whether they are used for face recognition.
• Space between two eyes: minimum 60 pixels, suggested more than maximum 90 pixels
• Brightness: no shadow on the face, no over-exposure, and under-exposure
• Contrast: dynamic range of gray level on the face should be 85~200
Intact Quality Metrics: Blur, Distortion, Objectness
Some intact quality metrics about the completeness of the raw image are also evaluated by the quality model and are compared with the pre-set criteria. Because the low value of these metrics means the raw image data loses information in these aspects, they cannot be restored by internal algorithms. Still, the tolerance threshold can be set up in the AI function manager to determine whether they are used for face recognition.
Advantages Of Using Artificial Intelligence
Feature extraction is the most critical step in facial recognition. The feature extraction module will generate peculiar face features for each person. We have improved and optimized models, datasets, and processes. With a residual neural network (refer to the following diagram) and millions of training samples, the algorithm archives 9th place for face recognition with mask and 17th place for 1:1 face comparison (data collected on June 22, 2021) in FRVT (Face Recognition Vendor Test). It can meet the customer's needs in a variety of scenarios.
Face Detection Performance
The indicators of the face detection algorithm are described by the detection rate and the false detection rate, and the two indicators jointly evaluate the face detection algorithm. Detection rate (also called recall rate): The number of correctly detected (positive) face samples is higher than the total number of positive (including face) samples. The missed detection rate is the opposite of the detection rate, and the missed detection rate = 1 – the detection rate. False detection rate (false detection rate): The total number of samples that negative examples (not faces) are considered positive examples (faces) compared to negative examples (not faces). In large-scale tests, the performance of the face detection algorithm is as follows:
- Face detection rate 99%
- Face detection error rate 0.05%
Face Recognition Performance
The Face Recognition Vendor Test (FRVT), conducted by the U.S. National Institute of Standards and Technology (NIST), was a series of large-scale independent evaluations for face recognition systems. FRVT measures face recognition performance by FMR and FNMR. It is one of the world's most authoritative face recognition systems tests. Up to now, nearly 200 companies and research institutions worldwide have participated in this test, including all the major face recognition companies. We have achieved a Top 5 ranking among 189 tested systems and 249 entrants for the 1:1 verification wearing masks in the latest Face Recognition Vendor Test (FRVT). It also ranked top 15 on the test of the wild face in unconstrained scenarios in the FRVT test, performing strongly at the extreme view and angle in complex surveillance scenarios on various lighting, distortion, blur issues, etc. The June 25, 2021 test results established that Remark AI is the Top 1 solution in the western world, outperforming billion-dollar unicorns. The following table shows the test results submitted with the remarkai-003 model on June 22, 2021.
Face recognition technology plays a pivotal role in preventing crime by enhancing law enforcement capabilities. Its ability to rapidly identify individuals in crowded public spaces aids in tracking and locating suspects. In scenarios such as criminal investigations, security forces can use facial recognition to match images captured on surveillance cameras with criminal databases, helping identify and apprehend individuals with outstanding warrants or connections to criminal activities. This proactive approach contributes significantly to crime prevention and public safety.
Ensuring public safety is a paramount benefit of face recognition technology. In crowded events, transportation hubs, and urban centers, it serves as a valuable tool for monitoring and responding to potential threats in real-time. The technology can be employed to detect individuals on watchlists, enhancing security measures and allowing for swift intervention. Additionally, face recognition aids in the quick identification of missing persons, ensuring a rapid and coordinated response in emergencies. By bolstering situational awareness, this technology fosters an environment where individuals can feel secure and protected.
Face recognition's positive influence on public safety has direct implications for the economy. A safer environment encourages economic activities by fostering public confidence, attracting investments, and promoting tourism. Businesses thrive in secure surroundings, leading to increased foot traffic, consumer spending, and economic growth. Moreover, the technology's applications in access control and fraud prevention contribute to safeguarding financial transactions and protecting businesses from illicit activities, thereby fostering a more robust and resilient economic landscape.
In the digital age, face recognition serves as a formidable tool in preventing fraud and bolstering cybersecurity. Industries such as finance and e-commerce leverage facial recognition for secure authentication processes, reducing the risk of unauthorized access or identity theft. This not only protects individuals but also fortifies the overall digital infrastructure, safeguarding critical systems and sensitive information from malicious actors.
Face recognition expedites law enforcement processes by automating the identification of individuals involved in criminal activities. This efficiency allows law enforcement agencies to allocate resources more effectively, respond promptly to incidents, and streamline investigations. The technology's ability to analyze vast amounts of data quickly enables law enforcement to connect disparate pieces of information, uncover patterns, and solve cases faster, contributing to a safer society.
While recognizing the significant benefits of face recognition, it is crucial to address privacy concerns and ensure ethical implementation. Striking a balance between security and privacy is essential to build public trust in the technology. Implementing robust data protection measures, obtaining consent where necessary, and adhering to ethical guidelines are essential components of ensuring that face recognition technologies are used responsibly and respect individuals' rights. A thoughtful and transparent approach in deployment ensures that the benefits of face recognition are realized without compromising privacy and civil liberties. Remark Holdings, Inc. (NASDAQ: MARK) is paving the way to an effective, responsible future.
REMARK HOLDINGS, INC. (NASDAQ: MARK) has strategically positioned itself in the market through key partnerships, enhancing its capabilities and market reach. The collaboration with Arrow Electronics and Intel signifies a notable milestone, enabling Remark to extend its Smart Safety Platform (SSP) to over 200,000 customers through Arrow's distribution network. This partnership facilitates the integration of Remark's AI-powered video analytics with Intel's high-performance processors, creating a comprehensive solution for real-time video analysis in various sectors, including public safety, security, and smart city initiatives.
The partnership with NVIDIA, where REMARK HOLDINGS, INC. (NASDAQ: MARK) joined forces with PNY to present at the Smart City Expo World Congress, illustrates the company's commitment to market expansion and cutting-edge technology. By participating in events of global significance, Remark not only showcases its products but also fosters collaboration with industry leaders, positioning itself as a key player in the smart city ecosystem. Such collaborations contribute to the company's growth and strengthen its presence in emerging markets.
REMARK HOLDINGS, INC. (NASDAQ: MARK) has demonstrated financial stability and resilience, as evidenced by its positive net income in the fiscal year ending December 31, 2021. The ability to weather economic challenges and generate positive net income reflects the company's sound financial management and effective business strategies. This financial strength positions Remark to pursue growth opportunities, invest in research and development, and navigate market fluctuations with confidence.
Financial strength is often coupled with a commitment to innovation. Remark's consistent investment in research and development is a testament to its forward-thinking approach. By allocating resources to enhance its AI-powered video analytics solutions, the company stays at the forefront of technological advancements. This commitment not only ensures the competitiveness of its current offerings but also positions Remark to adapt to evolving market demands.
The financial health of REMARK HOLDINGS, INC. (NASDAQ: MARK) is reflected in its stock performance and investor confidence. Positive financial results, coupled with strategic partnerships and technological advancements, contribute to a favorable perception among investors. The company's stock performance is an important indicator of its market standing and the trust it garners from the investment community.
Beyond short-term financial metrics, Remark's partnerships and financial strength contribute to its long-term viability. Sustainability in the business landscape involves not only profitability but also adaptability to changing market dynamics. Remark's ability to form strategic partnerships, maintain positive cash flows, and invest in innovation positions it as a company with the potential for sustained growth and longevity in the competitive landscape.
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