[ANN][ICO] FACETER – computer vision surveillance technology

[hush]

First time I bought this token from pre-ICO but unfortunately this project didn't get positive response from big investors and fall to very low level. But I am still buying this to reduce my lose by getting a good average price for this token.

[1713568737]

1713568737

[1713568737]

1713568737

[alicea]

Hi Everyone!

Significant moment - the corporate version of Faceter is installed successfully straight from the flash drive. If you want Faceter software in your business, just email us - sales@faceter.io.

Yours,
Faceter Team

The moment we were waiting for and now all we need is some good price movement on FACE and we are golden.

[Faceter]

How Face Recognition Actually Works

Hi, I'm Vladimir, Faceter's CTO. Today I would like to talk to you about the finer details of face recognition technology and how Faceter works. There are a lot of technical details ahead - don't say we didn't warn you!

Many people believe that deep neural networks recognise faces. Actually, that's not quite right. A neural network allows you to extract a set of unique key features (facial features vector) from a face. Only then it is possible to identify/verify a person by comparing these key characteristics.

Usually, the key features are a set of N numbers (N = 32–2048 - in our case N = 128).
The problem is that although we said that facial features vector is unique to each person, in fact, the vectors extracted from one person's different photos will be slightly different. Therefore, we cannot compare vectors element-wise.



A classic way of comparing two vectors is to obtain the Euclidean distance between them. Click on the link if you are interested in how it is actually calculated (https://hlab.stanford.edu/brian/euclidean_distance_in.html).

The lesser the distance between vectors is, the more similar the images will be. Therefore, to compare two people we will need to calculate the distance between their facial features vectors. If the distance is sufficiently small (less than a certain threshold), then we decide that it is the same person; if not, they must be different people.

Let's imagine that we have a database of 1 million people (it means that we have 1 million facial features vectors), and we need to find the most similar face to a reference person's face. In other words, we need to calculate 1 million vector distances between the reference person and the people in the database, and choose the smallest one. With such large volumes of data, this operation becomes quite resource intensive. Our R&D team wrote an implementation of the proximity search algorithm using the GPU, which shows very high performance - over 60 million comparisons per second. However, we are always searching for new ways to optimize our algorithms, and now we are examining an approach called DeepHash.



Using this approach, the neural network not only learns to extract facial vector features during the training process, but also learns to quantify the features of one person into a single unique hash that remains unique for every photo of this person. In this case, we can use the simplest hash comparison operation to compare facial hashes. This approach will create an incremental increase in the search/comparison performance of individuals.

If you are interested in such posts "from behind the scenes", leave your feedback, likes and feel free to repost. From time to time, my colleagues and I will be happy to share our thoughts, experiences, and the latest updates with you.

Stay tuned!

Vladimir,
Faceter CTO

[hazenyc]

How Face Recognition Actually Works

Hi, I'm Vladimir, Faceter's CTO. Today I would like to talk to you about the finer details of face recognition technology and how Faceter works. There are a lot of technical details ahead - don't say we didn't warn you!

Many people believe that deep neural networks recognise faces. Actually, that's not quite right. A neural network allows you to extract a set of unique key features (facial features vector) from a face. Only then it is possible to identify/verify a person by comparing these key characteristics.

Usually, the key features are a set of N numbers (N = 32–2048 - in our case N = 128).
The problem is that although we said that facial features vector is unique to each person, in fact, the vectors extracted from one person's different photos will be slightly different. Therefore, we cannot compare vectors element-wise.



A classic way of comparing two vectors is to obtain the Euclidean distance between them. Click on the link if you are interested in how it is actually calculated (https://hlab.stanford.edu/brian/euclidean_distance_in.html).

The lesser the distance between vectors is, the more similar the images will be. Therefore, to compare two people we will need to calculate the distance between their facial features vectors. If the distance is sufficiently small (less than a certain threshold), then we decide that it is the same person; if not, they must be different people.

Let's imagine that we have a database of 1 million people (it means that we have 1 million facial features vectors), and we need to find the most similar face to a reference person's face. In other words, we need to calculate 1 million vector distances between the reference person and the people in the database, and choose the smallest one. With such large volumes of data, this operation becomes quite resource intensive. Our R&D team wrote an implementation of the proximity search algorithm using the GPU, which shows very high performance - over 60 million comparisons per second. However, we are always searching for new ways to optimize our algorithms, and now we are examining an approach called DeepHash.



Using this approach, the neural network not only learns to extract facial vector features during the training process, but also learns to quantify the features of one person into a single unique hash that remains unique for every photo of this person. In this case, we can use the simplest hash comparison operation to compare facial hashes. This approach will create an incremental increase in the search/comparison performance of individuals.

If you are interested in such posts "from behind the scenes", leave your feedback, likes and feel free to repost. From time to time, my colleagues and I will be happy to share our thoughts, experiences, and the latest updates with you.

Stay tuned!

Vladimir,
Faceter CTO

That's good information provided here. I have a question though: what happens if people get older over time? Are the hashes presenting face characteristics sufficiently unique or could it happen that an older face will not be successfully recognized again?

[bellaobella]

This coin has been listed on @CoinMarketDaddy(CMD). For more information , metric ,Statistics, prices, reviews , online ticker , news, comments about this Coin visit

FACE Market & Trading Data
Market Cap   Current Supply   Volume (24h)   Total Supply          Price %(1h)   Price %(24h)   Price %(7d)
 $ 3.02M            469,085,040    $ 64.83K           38,641,610                0.16%            -3.45%            7.56%


https://coinmarketdaddy.com/currencies/token/faceter/face/

[Faceter]

Tech Update #4

Hello, everyone!
We have prepared again an update and exciting news for you about how Faceter software development is proceeding. Today, we will tell you how things are going in Android development, explain new functionality and show you a few fresh screenshots.

Debugging Before the Launch
Since the project is gradually approaching the stage of the first public release, we are increasingly focusing on the sustainability and the correct work of the application. If the last time we talked about a biggish list of bugs that had been fixed, then this time we have reworked the layout of both mobile applications. The new version became much lighter. It allowed us to increase applications’ effectiveness on all devices. Also, we simplified the interface. So as the test results show, the app is more intuitive now. Very soon, you will be able to test our achievements by yourself!

Android-Version Is On Board!
We are happy to announce that, as we assumed earlier, the Android-development team has overtaken the iOS team, and the recognition technology has started to work in the Android-version of the application. Yes, we still have a lot of work and tests to do, but another big target is behind.

New Features: Video Data Saving and ID Signing
There is a new function of playing the saved video in the web version and both mobile applications. Moreover, if the video was not saved for whatever reason, Faceter will notify you and even name the cause or error. By the way, as for the “missing fragments error” — if you have an original idea on the “screensaver” for the situation when the video has not been saved (poor connection, cut a piece, etc.), then please share it with us and the community. Use the hashtag #backout_faceter on social media — we will be very thankful for all your outstanding ideas. Please, do not offer an image of the girl from “The Ring” movie!

Besides, Faceter has one more important function — now, you can sign recognized people according to their names/nicknames from the profile if a camera has caught them. Being able to determine intruders is much more convenient!

New Screenshots


Finally, we are very excited to show you several new application screenshots. Do you have any comments on this? Then please do not hesitate to share them with us — as long as the development is in the process, we are particularly interested in what you are thinking about our project!

That’s all for now. Before the next tech update, you will receive a very important piece of news that will cheer you up and will not leave anyone indifferent. What is it exactly? Let’s say, there is a hint in the previous updates. However, it is not necessary to play detective — there is very little time left!

See you in a couple of weeks.

Yours,
Faceter team

[micz_bjukanon]

Tech Update #4

Hello, everyone!

We have prepared again an update and exciting news for you about how Faceter software development is proceeding. Today, we will tell you how things are going in Android development, explain new functionality and show you a few fresh screenshots.

Debugging Before the Launch
Since the project is gradually approaching the stage of the first public release, we are increasingly focusing on the sustainability and the correct work of the application. If the last time we talked about a biggish list of bugs that had been fixed, then this time we have reworked the layout of both mobile applications. The new version became much lighter. It allowed us to increase applications’ effectiveness on all devices. Also, we simplified the interface. So as the test results show, the app is more intuitive now. Very soon, you will be able to test our achievements by yourself!

Android-Version Is On Board!
We are happy to announce that, as we assumed earlier, the Android-development team has overtaken the iOS team, and the recognition technology has started to work in the Android-version of the application. Yes, we still have a lot of work and tests to do, but another big target is behind.

New Features: Video Data Saving and ID Signing
There is a new function of playing the saved video in the web version and both mobile applications. Moreover, if the video was not saved for whatever reason, Faceter will notify you and even name the cause or error. By the way, as for the “missing fragments error” — if you have an original idea on the “screensaver” for the situation when the video has not been saved (poor connection, cut a piece, etc.), then please share it with us and the community. Use the hashtag #backout_faceter on social media — we will be very thankful for all your outstanding ideas. Please, do not offer an image of the girl from “The Ring” movie!

Besides, Faceter has one more important function — now, you can sign recognized people according to their names/nicknames from the profile if a camera has caught them. Being able to determine intruders is much more convenient!

New Screenshots
 You can find some new screenshots in our Reddit account: https://www.reddit.com/r/faceterOfficial/comments/9j2yt0/tech_update_4/
Finally, we are very excited to show you several new application screenshots. Do you have any comments on this? Then please do not hesitate to share them with us — as long as the development is in the process, we are particularly interested in what you are thinking about our project!

That’s all for now. Before the next tech update, you will receive a very important piece of news that will cheer you up and will not leave anyone indifferent. What is it exactly? Let’s say, there is a hint in the previous updates. However, it is not necessary to play detective — there is very little time left!

See you in a couple of weeks.

Yours,
Faceter team

New Faceter app interface looks clean.
I'm waiting to test myself Android app when it will be released to the public.

Good work and do Your best to deliver it on time.

[AlloM]

Tech Update #4

Hello, everyone!

We have prepared again an update and exciting news for you about how Faceter software development is proceeding. Today, we will tell you how things are going in Android development, explain new functionality and show you a few fresh screenshots.

Debugging Before the Launch
Since the project is gradually approaching the stage of the first public release, we are increasingly focusing on the sustainability and the correct work of the application. If the last time we talked about a biggish list of bugs that had been fixed, then this time we have reworked the layout of both mobile applications. The new version became much lighter. It allowed us to increase applications’ effectiveness on all devices. Also, we simplified the interface. So as the test results show, the app is more intuitive now. Very soon, you will be able to test our achievements by yourself!

Android-Version Is On Board!
We are happy to announce that, as we assumed earlier, the Android-development team has overtaken the iOS team, and the recognition technology has started to work in the Android-version of the application. Yes, we still have a lot of work and tests to do, but another big target is behind.

New Features: Video Data Saving and ID Signing
There is a new function of playing the saved video in the web version and both mobile applications. Moreover, if the video was not saved for whatever reason, Faceter will notify you and even name the cause or error. By the way, as for the “missing fragments error” — if you have an original idea on the “screensaver” for the situation when the video has not been saved (poor connection, cut a piece, etc.), then please share it with us and the community. Use the hashtag #backout_faceter on social media — we will be very thankful for all your outstanding ideas. Please, do not offer an image of the girl from “The Ring” movie!

Besides, Faceter has one more important function — now, you can sign recognized people according to their names/nicknames from the profile if a camera has caught them. Being able to determine intruders is much more convenient!

New Screenshots
 You can find some new screenshots in our Reddit account: https://www.reddit.com/r/faceterOfficial/comments/9j2yt0/tech_update_4/
Finally, we are very excited to show you several new application screenshots. Do you have any comments on this? Then please do not hesitate to share them with us — as long as the development is in the process, we are particularly interested in what you are thinking about our project!

That’s all for now. Before the next tech update, you will receive a very important piece of news that will cheer you up and will not leave anyone indifferent. What is it exactly? Let’s say, there is a hint in the previous updates. However, it is not necessary to play detective — there is very little time left!

See you in a couple of weeks.

Yours,
Faceter team

New Faceter app interface looks clean.
I'm waiting to test myself Android app when it will be released to the public.

Good work and do Your best to deliver it on time.

Indeed this will be big moment for this project when that app will be available for testing. Their development progress is looking amazing so that I also have some good hope for the future of this platform.

[pumz]

This last update on development status is looking very positive and shows how advanced work has been reached. Each update is getting us more and more closer to launch of the application. This is huge industry and these surveillance technology build by faceter has potential and use case. On the other hand as launch is getting nearer tokens are becoming more cheaper good to fill the bag with.

[micz_bjukanon]

This last update on development status is looking very positive and shows how advanced work has been reached. Each update is getting us more and more closer to launch of the application. This is huge industry and these surveillance technology build by faceter has potential and use case. On the other hand as launch is getting nearer tokens are becoming more cheaper good to fill the bag with.

In addition to surveillance, the application may take place in other industries.
After the release of the full Faceter app and the operating API, many companies will be able to enrich their payroll systems, marketing campaigns, loyalty systems in stores automatically generating bonuses for regular customers and remember their preferences.
Such data in business is valuable.

For example, after entering your favourite clothing store, the salesperson immediately sees data collected during previous visits.
The tablet displays its dimensions, the history of recent purchases, the average price for which it usually buys.
It can be a chain of stores and in every branch of this network these data will be available.

[Ano]

Concept of this project is truly awesome it is at early stage of development that is why value of this token is not increasing. I hope release of the app this will get succeed to catch the attention of big investors and community will start growing.

[Faceter]

Tech Update #5

Hello Everyone!
As the finish line is getting closer and closer and the adrenaline is pumping, we can’t wait to share our latest news and achievements with you. Let's get started!

Work with the archive.
We have already mentioned the function of saving the video, but we have only finished working on it this month. Even if there is a lack of free space the system will continue to work stably, overwriting old material and giving priority to the last 24 hours of the video recording. We also updated the player with the archiving function and improved the user interface—we’re sure you’ll love it!

Express your opinion: we are thinking about implementing a new function that allows users to record a video only during the active moments (when there are any actions in the camera field). This will greatly optimize space management, but this option is not suitable for everyone. What do you think? Would you find this function useful?



Performance improvement.
Earlier we used third-party solutions to have an opportunity to work on the UI, but now we’ve switched to our own developments. This will allow our users to get better performance with less resources. For example, video playback function (HLS on Demand).

Time & Attendance.
There are new basic functions of Time & Attendance, and this feature also allows users to receive reports with repeated visits. To configure this new functionality, you hardly need to do anything—just set up the cameras. By comparison, with other similar solutions it is necessary to set up work shifts, work schedules for different groups of people, etc.
We also provide the real-time uploading of data from Faceter to any ready-made T & A systems. This can be very useful if some corporate clients currently use another system.

Recognition system update.
We continue to improve the algorithms of the recognition itself, primarily through the tracking of new technologies and approaches, testing and, if necessary, implementation. During this development session, the standout example of such work was the use of the Deep Wise Convolution layer in our latest version of the face detection network. The use of such layers in a neural network makes it possible to obtain a significant performance increase with minimal loss in quality of detection.

By the way, if you think that our work consists of sitting down behind the monitor, then you are mistaken! Developers of smart video surveillance systems have to overcome quite large distances at work. See for yourself.

Beta-test.
Finally, the time has come. We are pleased to announce that soon you to will be able to try out Faceter in the real world. We are finishing the final preparations, and very soon we will invite everyone who wants to participate in the beta testing of the full version of Faceter to be a part of this grand testing. You will need a suitable camera and a few minutes of free time to set up, and to learn about the start of the beta testing you will need to keep up to date with us on social media.

That's all for today! See you again in a few weeks, as well as at the upcoming beta testing.

Yours,
Faceter Team

[Faceter]

The new website is now on!

Hello everyone!

This is Rob and the team. As we planned earlier, today, on November 14, we have launched a new version of our website. So this is a significant moment for us.

This year was an exciting adventure, but now Faceter has grown out of just the “blockchain startup” status. We have been convinced from the very beginning that Faceter is a revolutionary product that will become an essential part of the everyday life of our customers.
The new website emphasizes this. We have tried to reflect what is unique about Faceter, how it works and who may need our product.

If you are waiting the Faceter’s mass-market software release as much as we are, this new website is the first step in this way. Go to New Website right now and check out all the new things and features.

Share your opinions and suggestions about faceter.io! Send your feedback right to marketing@faceter.com - not a single letter will remain without our attention!

Thank you all!

Yours,
Rob and the Team

[Faceter]

Q&A #3

Hello everyone!
Here we answer your most common questions. We noticed that you often ask us about the software itself, its price, and different versions. You ask about how it looks now, how it will look at the launch, and what will happen after the mass-market version is released. Well, let's shed some light on these topics...

Are the functionality and interface of the corporate and mass-market versions different?
Good question. For now, we have decided to develop both versions within a single codebase since we are working on key functions that are important for both development branches. Further, the corporate and mass-market versions will again be separated. However, most of the functions and their UI representation will stay the same.

How much is the corporate version?
The cost of the corporate version depends on the number of licensed cameras. The cost of one license, including integration, starts from $30 per month.

Does the B2B version use a decentralized network or centralized servers?
Both options are possible, depending on the customer’s preferences.

Will the B2C version be updated on a regular basis?
Of course. We are currently working on convenient mobile applications for iOS and Android, as well as on affordable cloud storage for the mass market. However, we are not going to stop there and will continue to improve Faceter. We have something to surprise you with;)

Can I install the app and have a look?
Yes! You will have such opportunity soon. Prepare your CCTV cameras, because beta-testing is coming;)

That's all for today!

Please share your questions and ideas with us via social media, or email ask@faceter.io. We’d love to hear from you!

Faceter Team

[Faceter]

Hi, everyone!

We’re really excited to announce that we’re finally ready to start the accumulation of data for beta testing. Most importantly, we’re ready to test Faceter software on real CCTV systems for the benefit of our future customers. That’s where you come in!

We are inviting everyone who wants to participate and has all the necessary skills and equipment, to take part in the closed beta-testing of our product. It’s an excellent opportunity to become one of the first to try Faceter’s smart video surveillance for the home, office, or production facility.

Requirements:
- You are a confident PC user, and able to follow instructions carefully.
- You have at least one CCTV camera (NOT wide-angle and NOT dome).

If this sounds like you, please click this link and fill in the questionnaire. Our support team will contact you as soon as possible, once everything we need will be ready for the test.

Finally, we’d like to thank all of you who have supported us up to now. We hope you’re just as excited as we are about taking the next step towards a fantastic future of Faceter!

[Faceter]

Hello everyone!
The new video is on now! It's time to summarize and set our goals for 2019. The company's CEO Rob Pothier will reveal all the details and personally show the current version of the Faceter app for iOS. Enjoy!

Check out the video here

[siaynoq]

 A pet peeve, and a red flag usually, when the copyright date at the bottom of the webpage isn't current.  No biggie, but it's enough to have someone really scrutinize things further.

[Faceter]

Tech News from RnD

Hello Everyone!
As the time goes, our developers are preparing new features for both Business-to-Business and Business-to-Customer solutions. Today we are happy to tell you about our new Business-to-Business tool, aimed to help mainly in Sales, Marketing and Workforce management.

Have you ever wished to know how much time people need in average to order pizza or choose milk in the supermarket? Or why receptionist always mopes around, but is never on his place to register you? Our devs wish to know that too and to allow our customers to know that. They thought of a tool, that would allow to analyse the customer attendance and get the proper statistics on the time spent in each customised zone or the tool, that would allow to track the crew behaviour in the customer area. That is how that feature was born in our RnD department.

If you work with video surveillance, you might know, that many cameras or surveillance providers do support a kind of the same feature, but you are drown in countless in range notifications, border cross notifications etc. etc. Our dev team wants to convert this chaotic information flow into a useful and meaningful analytic tool, that can be utilized for several business needs.

There are other features already integrated to the Business-to-Business solution, but they do not allow to track specific zones and apply analytics to them. By applying this feature user receives an integrated analytics tool, that foster sales by better merchandising or factor into changes in workforce management.

See how it looks like to have different zones on the camera stream:


Timeline
This feature is specifically designed to handle analytics, that is why user needs to set up two main parameters once and system will be all set:

• define zones “Customer zone”, “Merchant zone”, “Pastry zone” etc.;
• set up different timeframes , such as “Working Hours”, “Closed” etc.

Hereinafter the application will show appearance analytics based on this times and zones.
See for yourself:




Comprehensible Analytics
People want to have vivid graphics instead of numbers , that is why we have also added a board, that shows attendance time on a pie chart:


Each tool helps business to sell and all of us to work more efficient and have more free time, that you’ve used to read this article. If you are interested in more tech details of this solution — do not hesitate to comment and we will prepare our next material on some dev details of our new feature.

Yours,
Faceter Team

[CORNEL]

I would like to ask what is the new strategy of the team to promote this nothing improved from long time trade volume is still very disappointing. I think it was my bad decision to invest in this project at the time of ICO.

[cryptofoxes]

At this time, almost all of my investments now look like this. Yes, it is much lower than the price from the sale of ico. I hope when there will be growth and when you launch your product, we will resume the normal volume.