In today’s world business faces a lot of security issues. It can be divided into two big categories:
- Virtual Problems (Hacking mostly)
- Real-Life Problems (Robbery, Shortages and so on)
To fix these problems businesses from any part of the world, should spend millions of dollars to hire security, use video surveillance systems and other types of technologies. Faceter which we will evaluate today is going to take its market share right on the market of video surveillance systems. They were working hard for the last 3 years and already have a Minimum Viable Product ready-to-use. Let’s have a closer look at their solution. To do this, let’s identify the existing problem, declared by the team of the project.
The current state of the video surveillance systems is very low in terms of available technologies. It’s just a huge video archive which will be overwritten any time when the hard disk is full. In case of any crime, everything what policeman can do is to watch the video, catch the moment with his face (probably in low-definition) and send this face to the other branches of policemen. So, the main problem in the current market of the CCTV could be described as follows:
CCTVs are not connected with an intelligent software to process it in a real-time (online). So there is no ability to analyze events occur in such video and, a critical point for crimes, time is missing.
To prolong this vision further, we can add the following components to the problem:
- In case of any crime, CCTV should be able to identify that something is going wrong and to have face recognition function.
- After face recognition and criminal identification, CCTV and software should be able to provide the list of previously visited places to identify the route and previously visited places by the criminal.
Based on this problem Faceter is going to develop their own complex solution for facial recognition using provided CCTV infrastructure.
What features do they declare?
- Behavioral analysis based on the object and facial recognition technology. Faceter has mentioned that their algorithms have passed tests provided by Label Faces in the Wild (LFW (http://vis-www.cs.umass.edu/lfw/)), and MegaFace (http://megaface.cs.washington.edu/) but they didn’t provide any proven records of this or even screenshots.
- “Fog” Computing. To make calculations within the system, Faceter will use distributed network of miners, who will unite their computing power into one huge ecosystem to provide all necessary calculations for the network of CCTV. This feature, probably, will make this technology affordable either for businesses or private persons.
- Machine Learning. To make CCTV “smarter” they will use Machine Learning algorithms to constantly improve their technology
- Mobile application alongside with web-interface to control your own network of CCTV.
What differs Faceter from the others in the market?
First of all, Faceter declared that their facial recognition system has passed difficult and valuable tests like MegaFace and LFW but, as we mentioned before, there is no links or screenshots to the result of these tests. Secondly, using of blockchain with its mining ability they can provide computational power at lower costs than traditional systems do. Important detail: they need to have enough adopters to build a definitely huge infrastructure of miners. The third point is using of Machine Learning, so Faceter product will study from case to case and when necessary it will use its knowledge (e.g. Call 911 in case of crime or send SMS to the landlord if there is a fire at home). And finally, Faceter system will have an ability to be integrated within all existing CCTV systems all around the Globe.
Actually, we’re not sure that other players on the market cannot call 911 or do not have their own API for integration. So, basically, Faceter has only one competitive advantage – it’s “Fog” Computing – in other words, it’s mining network to provide huge computational power to the Faceter end users.
Part #2: Implementation and Use Cases.
Faceter has prepared to its launch very carefully. In the world where mostly ICO launch coming earlier than product, Faceter choose the other way. They’ve got ready to use MVP and it has passed the different test and now available to everyone who needs it.
According to their whitepaper, we have identified the most common use cases to evaluate potential market and growth of the company. Here are the most obvious use cases for the Faceter platform. They’re categorized for B2B, B2C, and even B2G segments.
- B2B. The most simple and useful use case of the platform is – Personnel Management. Using Faceter product, each company/factory/hotel can control their staff. Where they spend most of their working time, what do they actually do and even prevents thefts.
- B2C. Use of Faceter at home can have different goals. Someone wants to know how is his granny because she is old, another wants to know what’s going on with his child when he or she is at work. This list could be infinite and depends only on human’s imagination.
- B2G. It is the most important and, probably, profitable way of using Faceter product. Speed cameras, Traffic control cameras, CCTV to control crowds and so on. Government like to control and Faceter should provide them with such ability.
(Auth. Note. – So irony, that Government will control people using the technology of freedom – blockchain.)
Part #3: Market Assumption.
From the previous part, you saw that field of Faceter product implementation is almost infinite because using of CCTV systems is widely adopted and enhanced the function of these systems will be great for current owners. Let’s have a look at the token model of Faceter.
As all of you know, to prevent inflation and to increase the price of the token, companies are using “Burning model”. It means that the number of tokens (token supply) will decrease through the burn of its part. Faceter declared that they’ll burn 20% of each payment made by their end customers. It means, that if you are ordering their service and negotiate payment after you’ve made this payment, Faceter will allocate it as follows:
- 60 % of the payment will be allocated to the Reserve Fund
- 20 % will cover costs for processing your CCTV system
- 20 % will be burned to increase deflation.
Until we do not know exact prices for future services of Faceter, we cannot identify the exact percentage of deflation, so we can’t assume the potential increase in prices. We’ve reached Faceter team with this question and received an answer that predicted deflation rate is around 6-7 % per year. It means that every year project will burn up to 7 % of the total token supply (which is 1 000 000 000 FACE tokens – Auth. Note). Using this data we can assume that fair increase in prices of FACE tokens will be around 8-10 % per year without any pump/dump schemes. Because of huge market opportunities, Faceter can exceed these figures if they will reach wide adoption. Of course, do not forget about hi-tech corporations which are also working in this field, but early adoption of blockchain gives Faceter competitive advantage in this rally.
Part #4 Team of the project
The Faceter team is big enough to achieve all declared goals in their RoadMap. Interesting point that executive team is from all over the world, but tech team has been formed with only Russians and one of the main advisers is also Russian guy, from one of the most useless business incubators in the world – Skolkovo. His name is Igor Karavev. He is former Executive Director in Skolkovo and now is working with different blockchain project and Faceter is of them. Let’s have a look at the executive team:
- Robert Pothier. He has graduated from Damelin College one of the biggest educational centers in Africa. No information on the web about this guy and his previous experience. The same as suspicious information which also does not exist on the web.
- Paul Scott. This guy is known better than Mr. Pothier. His previous experience was at Polymorphic group as a managing director. He was working within fintech area and this project is quite related to his previous experience.
- Jason Gouws. He is sale manager of the Faceter, basically. This guy is responsible for mass adoption and support of their service. His previous experience is also limited to the written in LinkedIn profile.
That is an executive team of the project. The technical team is on the backend and there are about 8 people who is working behind the scene. All of them has a LinkedIn profile and common detail – they all are Russians.
Part #5 Conclusion and ICOguru recommendation
Charlie Lee (Litecoin creator – Auth. Note) has said that in cryptocurrency market it’s impossible to predict on the short-term and we, at ICOguru, are totally agreed with him. So all that we’re talking about is a long-term prediction. Faceter is a long-term product too (full functionality will be available only in 2019 – Auth. Note). Now, they have closed presale stage in less than a minute and collected around $ 10M, still more than $ 30M left for crowd sale.
We’re going to use the following scheme when buying FACE tokens:
- Entire investment will be around 0.5 ETH (around 5 500 FACE tokens)
- At ICO stage we’re going to invest 0.2 ETH and will wait when tokens will be listed.
- When Faceter will be on exchanges, we’ll buy tokens for the left 0.3 ETH after a panic sale of those who came just for speculations. We predict that price in first days after listing will decrease by 30 % as a minimum. At this level, we’ll enter and will hold it in our portfolio.
- The period is around 18 months with an expected return around 600 %.
Disclaimer: This review has been made for personal purposes and cannot be used as investment recommendation or advice. You should be aware of all possible risks when participating in ICO and entire crypto market.