facial recognition terminal lcd panel free sample

Facial recognition with liveness detection simplifies online onboarding and KYC procedures. Thales is a major provider of identity verification solutions, including this feature.

Since 2017, KFC, the American king of fried chicken, and Chinese retail and tech giant Alibaba have been testing a face recognition payment solution in Hangzhou, China.

In March 2021, 52 Perekrestok stores (Перекрёсток)  from X5 retail group have launched touchless payment by face for self-service checkout terminals with Visa Payment System and Sberbank.

While the United States currently offers the largest market for face recognition opportunities, the Asia-Pacific regionsees the fastest growth in the sector. China and India lead the field.

Moscowclaims one of the world’s largest networks of 160,000 surveillance cameras by the end of 2019 and is fitted with facial recognition technology for public safety.

In July 2018, Bradford L. Smith, Microsoft’s president, compared the face recognition technology to products like highly regulated medicines, and he urged Congress to study it and oversee itsuse.

In May 2019, US Rep. Alexandria Ocasio-Cortez voiced her "absolute" concerns in a recent Committee Hearing on facial recognition Technology (Impact on our Civil Rights and Liberties).

Privacy and civil rights concerns have escalated in the country as face recognition gains traction as a law enforcement tool, and on 6 May 2019, San Francisco voted to ban facial recognition.

As reported by theBoston Globe on 27 June 2019, the Somerville City Council (Massachusetts) voted to ban facial recognition, making the city the second community to make such a decision.

On 16 July 2019, Oakland (California) took the same decision and became the third US city to ban face recognition technology. It is interesting to note that the Oakland Police department is not using this technology and was not planning to use it.

San Diego took the same decision at the end of December 2019 in advance of the new Californian law. This new law (Assembly Bill 215) about facial recognition and other biometric surveillance) specifically prohibits the use of police body cameras in California. The ban is in place for three years as of 1 January 2020.

Virginia legislature passed (April 2021) a new bill (HB 2031) that prohibits law enforcement agencies from continuing to use facial recognition software after 1 July 2021.

In Europe, at the end of August 2019, Sweden"s Data Protection Authority decided to ban facial recognition technology in schools and fined a local high school (the first GDPR penalty in the country).

The EU Commission is planning to act on the indiscriminate use of facial identifier technology. The European Commission president Ursula von der Leyen wants a coordinated approach to artificial intelligence"s human and ethical implications. She has pledged to publish an AI legislation blueprint very soon.

Grigory Bakunov in Russia has invented a solution to escape proper face detection and confuse face detection devices. He has developed an algorithm that creates special makeup to fool the software. However, he has chosen not to bring his product to market after realizing how easily criminals could use it.In Germany, Berlin artist Adam Harvey has come up with a similar device known as CV Dazzle. He is now working on clothing featuring patternsto prevent detection. The hyperface camouflage includes patterns in fabric, such as eyes and mouths, to fool the face recognition system.In late 2017, a Vietnamese company successfully used a mask to hack the Face ID face recognition function of Apple"s iPhone X. However, the hack is too complicated to implement for large-scale exploitation.Around the same time, researchers from a German company revealeda hack that allowed them to bypass the facial authentication of Windows 10 Hello by printing a facial image in infrared.​Forbes announced in an article from May 2018 that researchers from the University of Toronto have developed an algorithm to disrupt facial recognition software (aka privacy filter).In August 2020, the Verge detailed a "cloaking" app named Fawkes. The software imperceptibly distorts your selfies and other pics you may leave on social media. The tool is coming from the University of Chicago’s Sand Lab.In short, a user could apply a filter that modifies​ specific pixels in an image before putting it on the web. These changes are imperceptible to the human eye but are very confusing for facial recognition algorithms.

In November 2020, a tool named Anonymizer was made available by Generated Media. The software creates a series of synthetic portraits from a picture you can upload. The images are mathematically similar to your face and look like you but will trick facial recognition software, according to tnw website. It could be an interesting solution to fool systems like Clearview AI that are scrapping millions of faces from social media (learn more on the Clearview AI controversy).

If you"ve something to say on facial recognition, tech, trends, a question to ask, or have simply found this article useful, please leave a comment in the box below.

Authorized access has come a long way from using keys, pin codes, cards, and fingerprints. We now find ourselves stepping into the era of more advanced biometric recognition. The use of face recognition in access control and time attendance has been quite popular in the market, mostly for its advantageous "touch-free" experience. Later, as access control continued to develop in biometric recognition, iris recognition became a new trend – especially for scenarios that require higher security and more convenience.

With advanced deep learning algorithms and practical functions, Hikvision"s MinMoe Face Recognition Terminals and Iris Recognition Terminals improve security and efficiency in access control and time attendance, making them easier than ever!

Empowered by deep learning algorithms, Hikvision MinMoe Face Recognition Terminals can reach an amazing recognition speed of 0.2 seconds, with the accuracy rate of over 99%. Equipped with dual lenses, the MinMoe terminals perform equally well under low or even zero-light environments.

Hikvision MinMoe Face Recognition Terminals support quick and easy registration via multiple access methods, and convenient device configuration via web or GUI as well. More conveniently, users can remotely check live view, unlock door, check event logs and more, via the Hik-Connect App on their smartphone.

Many factories use strict access control systems for both safety and confidentiality. But conventional methods of verification aren"t as reliable and convenient as today"s facial recognition. For example, employees lose their ID cards often, which brings potential security risks. Fingerprint authentication can also be inconvenient when staff members are carrying boxes or wearing gloves. Using face recognition terminals resolves all these problems at once - and adds security and convenience wherever it"s needed.

Access control and attendance are equally important to office areas. With the traditional card-based solutions, it"s easy for employees to fraudulently clock-in and clock-out. And fingerprint-based solutions may not be efficient at peak hours. Now, MinMoe Face Recognition Terminals create a reliable and efficient solution for office scenarios, with quick recognition speeds under 0.2 seconds.

In schools today, especially kindergartens and elementary schools, security is a top priority. Face recognition at the school gate guarantees that only authorized students, staff, and parents can enter the campus. Using facial imaging as the credential creates a superior security solution because, using Hikvision"s advanced technology, it"s virtually impossible to fabricate successful counterfeits.

Ensuring accurate and authentic attendance records is critical for store managers. The new MinMoe Value Series Face Recognition Terminals from Hikvision is the ideal solution for retail store applications of all kinds. It"s accurate, fast, and convenient for employees at all levels.

Many factories use strict access control systems for both safety and confidentiality. But conventional methods of verification aren"t as reliable and convenient as today"s facial recognition. For example, employees lose their ID cards often, which brings potential security risks. Fingerprint authentication can also be inconvenient when staff members are carrying boxes or wearing gloves. Using face recognition terminals resolves all these problems at once - and adds security and convenience wherever it"s needed.

Access control and attendance are equally important to office areas. With the traditional card-based solutions, it"s easy for employees to fraudulently clock-in and clock-out. And fingerprint-based solutions may not be efficient at peak hours. Now, MinMoe Face Recognition Terminals create a reliable and efficient solution for office scenarios, with quick recognition speeds under 0.2 seconds.

In schools today, especially kindergartens and elementary schools, security is a top priority. Face recognition at the school gate guarantees that only authorized students, staff, and parents can enter the campus. Using facial imaging as the credential creates a superior security solution because, using Hikvision"s advanced technology, it"s virtually impossible to fabricate successful counterfeits.

Ensuring accurate and authentic attendance records is critical for store managers. The new MinMoe Value Series Face Recognition Terminals from Hikvision is the ideal solution for retail store applications of all kinds. It"s accurate, fast, and convenient for employees at all levels.

Value Face Terminals offer limited storage capacity for facial images, but complete access control and time attendance functions. They can be very suitable and cost-effective for medium and small companies and retail establishments.

With a large storage capacity for facial images, high performance and comprehensive access control and time attendance functions, the Ultra Series Face Terminals offer users and clients an excellent user experience.

Pro Series Face Terminals Offers adequate storage capacity for facial images and complete access control and time attendance functions, giving this series the advantages of high cost-effectiveness and high adaptability to different application scenarios.

The MinMoe Face Recogntion Modules for Turnstile can easily integrate with turnstiles to provide the user with a complete access solution. The integrated terminal-turnstile solution is particularly useful for commercial buildings, banks and so on.

MinMoe Face Recognition Terminals are ideal for third-party software developers to integrate with their systems and applications for access control or time & attendance.

Five helpful methods are available for integrating your system with MinMoe Face Recognition Terminals: Intelligent Security API, Access Control Gateway API, Hik-ProConnect OpenAPI, HikCentral Professional OpenAPI, and HikCentral Professional Database/CSV/TXT.

Highly relevant features offered by TITAN include multi-factor authentication using any combination of multiple biometrics (face recognition, finger vein, fingerprint), mobile credentials (digital card, dynamic QR code), and traditional credentials (card, PIN). When using touchless credentials, TITAN’s speed is unmatched, helping you reduce crowding and improve convenience. TITAN’s flawless user experience is rounded out by rugged construction, modern design, and enough power built-in to future-proof your Invixium security system for years to come.

Touchless face recognition is IXM TITAN’s defining feature with a throughput of 15-18 users per minute and a 1:N face recognition capacity of 100,000 individuals. TITAN’s signature speed cuts down on the time shift changes or gaining access take, and its touchless functionality helps protect staff and visitors from germs spread by touching common surfaces.

Why secure your business with one biometric or credential when you can have two? IXM TITAN offers up to 2 biometrics: face recognition and an option of fingerprint or finger vein recognition. Multimodal authentication best suits applications in need of heightened security like financial institutions and data centers.

In a COVID-19 world, health conscious managers should do everything they can to eliminate the need for staff and visitors to take their masks off. So, IXM TITAN comes built with functionality to grant or deny access based on if a person is – or isn’t – wearing their mask. This feature compounds with TITAN’s touchless face recognition to offer face recognition while wearing a mask, allowing for hands-free, mask-on authentication.

When paired with the Enhancement Kit, IXM TITAN can perform simultaneous face recognition and elevated body temperature detection at the tear duct – the most accurate location on the body to touchlessly screen temperature. The Enhancement Kit is an accessory purpose-built for the TITAN with an integrated Thermographic Camera to scan for elevated body temperature during face recognition with a +/- 0.5 ⁰C (0.9 ⁰F) accuracy.

IXM TITAN also supports customizable attestation questions for health, safety, and compliance in two ways. First, TITAN can read the QR code generated by IXM Mobile after attestation questions are remotely completed by your staff and visitors. Second, IXM TITAN can display attestation questions on its LCD to be answered on-site. Ensure your staff is healthy with wellness questions, check to make sure workers are wearing protective gear, and more!

IXM TITAN is IP67 and IK10 rated for ingress and impact protection, making this solution ideal for challenging outdoor conditions. Paired with an all-aluminum body for design and effective heat-sink, as well as a 5” Corning® Gorilla® Glass LCD, TITAN offers peace of mind that the elements and rugged work environments will not harm your security system.

IXM TITAN is the premier Healthy Access solution by Invixium. Minimize the risk of illness spreading at the workplace with touchless face recognition while wearing a mask, mask detection and temperature screening from TITAN and the Enhancement Kit. High throughputs keep lines moving during critical times, allowing for social distancing and reducing risk of crowding in small areas.

Favored by the consumer electronics industry for its scratch resistance, Corning® Gorilla® Glass ensures equal parts elegance and ruggedness for IXM TITAN’s 5” LCD. The colossal LCD with 1080p HD resolution gives TITAN an awe-inspiring display and is used for PIN access, function key inputs and enrollment from the device among other things. The in-plane switching (IPS) technology provides a wider viewing angle and stunning colors even in bright sunlight.

In addition to 2 biometric modalities - facial recognition and an option of fingerprint or finger vein recognition, IXM TITAN also supports use of PINs and multiple card formats including iCLASS, Prox, MiFare and DESFire cards.

IXM TITAN boasts a 1:N face recognition and fingerprint capacity of 100,000 individuals each, or a 1:1 capacity of 500,000 for face and 1 million for fingerprint.

Your access control panel controls various actions taken by IXM TITAN such as sounds and LEDs, all of which are customizable through TITAN’s companion software, IXM WEB.

Please make sure that you have proper lighting to make the face recognition process easier and more efficient. Also, when enrolling a new face, you need to be steady and don’t move much, so that it properly saves your face features and can recognize it in the future.

The dl_lib.h is related to the face recognition capabilities 2 (esp-face), and it was removed in version 1.0.3 of the Arduino core. That said, just comment it out and it should compile and work perfectly either if you are using the Arduino IDE. Other option is to revert to version 1.0.2 of the arduino core.

very good work, it works for me, but the problem it is not saving recognized faces to microsd-card. Each time power on we have to start recognition again.

You have written a great introduction. Thanks! Could the face recognition feature be trained to recognize a stationary logo (not a face) on a TV screen? Is there any gpio output when a face is recognized?

I have got face recognition working after implementing a suggestion to download the ESP32 library to 1.04, the other suggestion to use 1.06 and edit the code did not work.

As we’ll see, the deep learning-based facial embeddings we’ll be using here today are both (1) highly accurate and (2) capable of being executed in real-time.

For the dlib facial recognition network, the output feature vector is 128-d (i.e., a list of 128 real-valued numbers) that is used to quantify the face. Training the network is done using triplets:

Our network architecture for face recognition is based on ResNet-34 from the Deep Residual Learning for Image Recognition paper by He et al., but with fewer layers and the number of filters reduced by half.

Both Davis King (the creator of dlib) and face_recognition module we’ll be using shortly) have written detailed articles on how deep learning-based facial recognition works:

The dlib library, maintained by Davis King, contains our implementation of “deep metric learning” which is used to construct our face embeddings used for the actual recognition process.

Note: For the following installs, ensure you are in a Python virtual environment if you’re using one. I highly recommend virtual environments for isolating your projects — it is a Python best practice. If you’ve followed my OpenCV install guides (and installed virtualenv + virtualenvwrapper ) then you can use the workon command prior to installing dlib and face_recognition.

output/: This is where you can store your processed face recognition videos. I’m leaving one of mine in the folder — the classic “lunch scene” from the original Jurassic Park movie.

videos/: Input videos should be stored in this folder. This folder also contains the “lunch scene” video but it hasn’t undergone our face recognition system yet.

First, we need to import required packages. Again, take note that this script requires imutils, face_recognition, and OpenCV installed. Scroll up to the “Install your face recognition libraries” to make sure you have the libraries ready to go on your system.

If you’re new to PyImageSearch, let me direct your attention to the above code block which will become familiar to you as you read more of my blog posts. We’re using argparse to parse command line arguments. When you run a Python program in your command line, you can provide additional information to the script without leaving your terminal. Lines 10-17 do not need to be modified as they parse input coming from the terminal. Check out my blog post about command line arguments if these lines look unfamiliar.

OpenCV orders color channels in BGR, but the dlib actually expects RGB. The face_recognition module uses dlib, so before we proceed, let’s swap color spaces on Line 37, naming the new image rgb.

Lines 41 and 42 actually find/localize the faces of her resulting in a list of face boxes. We pass two parameters to the face_recognition.face_locations method:

Then, we’re going to turn the bounding boxes of Ellie Sattler’s face into a list of 128 numbers on Line 45. This is known as encoding the face into a vector and the face_recognition.face_encodings method handles it for us.

This script requires just four imports on Lines 2-5. The face_recognition module will do the heavy lifting and OpenCV will help us to load, convert, and display the processed image.

We attempt to match each face in the input image (encoding) to our known encodings dataset (held in data["encodings"]) using face_recognition.compare_faces (Lines 40 and 41).

If the distance is below some tolerance (the smaller the tolerance, the more strict our facial recognition system will be) then we return True, indicating the faces match.

Using your terminal, first ensure you’re in your respective Python correct virtual environment with the workon command (if you are using a virtual environment, of course).

As I mentioned in our “Face recognition project structure” section, there’s an additional script included in the “Downloads” for this blog post — recognize_faces_video_file.py.

I applied our face recognition code to the popular “lunch scene” from the original Jurassic Park movie where the cast is sitting around a table sharing their concerns with the park:

Note: Recall that our model was trained on four members of the original cast: Alan Grant, Ellie Sattler, Ian Malcolm, and John Hammond. The model was not trained on Donald Gennaro (the lawyer) which is why his face is labeled as “Unknown”. This behavior was by design (not an accident) to show that our face recognition system can recognize faces it was trained on while leaving faces it cannot recognize as “Unknown”.

The face recognition method we used inside this tutorial was based on a combination of Davis King’s dlib library and Adam Geitgey’s face_recognition module.

Davis has provided a ResNet-based siamese network that is super useful for face recognition tasks. Adam’s library provides a wrapper around dlib to make the face recognition functionality easier to use.

However, there are other face recognition methods that can be used, including both deep learning-based and traditional computer vision-based approaches.

To start, take a look at this tutorial on OpenCV Face Recognition which is a pure OpenCV-based face recognizer (i.e., no other libraries, such as dlib, scikit-image, etc., are required to perform face recognition). That said, dlib’s face recognizer does tend to be a bit more accurate, so keep that in mind when implementing face recognition models of your own.

These methods are less accurate than their deep learning-based face recognition counterparts, but tend to be much more computationally efficient and will run faster on embedded systems.

Additionally, we made use of Davis King’s dlib library and Adam Geitgey’s face_recognition module which wraps around dlib’s deep metric learning, making facial recognition easier to accomplish.

There are a number of potential uses for facial recognition which is not only security system, but also communication robot, access management system, and various personal information management system. For instance, the functions mechanism is measuring the positions of feature points from an image of the captured face to detect the inclination and position.

This license is limited to a time in case of developing a trial product and suggesting a project at introducing the facial recognition. You can use all functions of the TeraFaces™ for Android by limiting to the time and the equipment built in Android OS.

This module has the micro-controller which is embedded in TeraFaces™’s function, the interface to a camera, the interface to a LCD panel and the general purpose I/O of connecting to the user’s host micro-controller. The size of it is only 3cm square.

This has a set of camera module and LCD panel to be able to evaluate immediately for the customer. And it has a GUI tool in order to evaluate easily the module.

This license is a time limited version in case of developing for a trial product and suggesting a project at introducing the facial recognition. You can use all functions of the TeraFaces™ for Android™ and the equipment built in Android OS.

This module integrates the micro-controller which is embedded in TeraFaces™’s function, the interface to a camera, the interface to a LCD panel and the general purpose I/O of connecting to the user’s host micro-controller.

In this article, Adeneye David Abiodun explains how to build a facial recognition web app with React by using the Face Recognition API, as well as the Face Detection model and Predict API. The app built in this article is similar to the face detection box on a pop-up camera in a mobile phone — it’s able to detect a human face in any image fetched from the Internet. Please note that you will need to know the fundamentals of React.

If you are going to build a facial recognition web app, this article will introduce you to an easy way of integrating such. In this article, we will take a look at the Face Detection model and Predict API for our face recognition web app with React.

Facial recognition is a technology that involves classifying and recognizing human faces, mostly by mapping individual facial features and recording the unique ratio mathematically and storing the data as a face print. The face detection in your mobile camera makes use of this technology.

Facial recognition is an enhanced application bio-metric software that uses a deep learning algorithm to compare a live capture or digital image to the stored face print to verify individual identity. However, deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces.

Facial detection is the process of identifying a human face within a scanned image; the process of extraction involves obtaining a facial region such as the eye spacing, variation, angle and ratio to determine if the object is human.

Note: The scope of this tutorial is far beyond this; you can read more on this topic in “Mobile App With Facial Recognition Feature: How To Make It Real”. In today’s article, we’ll only be building a web app that detects a human face in an image.

In this tutorial, we will be using Clarifai, a platform for visual recognition that offers a free tier for developers. They offer a comprehensive set of tools that enable you to manage your input data, annotate inputs for training, create new models, predict and search over your data. However, there are other face recognition API that you can use, check here to see a list of them. Their documentation will help you to integrate them into your app, as they all almost use the same model and process for detecting a face.

This is the final section of our web app where we get our facial recognition to work fully by calculating the face location of any image fetch from the web with Clarifai FACE_DETECT_MODEL and then display a facial box. Let open our src/App.js file and include the code below:

I hope you enjoyed working through this tutorial. We’ve learned how to build a face recognition app that can be integrated into our future project with more functionality, you also learn how to use an amazing machine learning API with react. You can always read more on Clarifai API from the references below. If you have any questions, you can leave them in the comments section and I’ll be happy to answer every single one and work you through any issues.