Faceter

Joint Surveillance Markets Revolution

[youtube https://www.youtube.com/watch?v=VR7JY9z6_Bc?rel=0&w=560&h=315]

CS Score

8.0

Outlook:

Positive

Faceter – by ,
8.0/ 10stars

Ended

2018/03/10 - 2018/04/30
ICO
Faceter
Token
FACE
Price
1 FACE = 0.1 USD
Platform
Ethereum
Accepting
BCH, BTC, DASH, ETH, LTC, XEM, XMR, XRP
Bounties
Not Available
Bonus
Available
Soft Cap
5,000,000 USD
Country
South Africa

CS Score :

8.0

Outlook :

Positive

Idea 50%
Idea 7.8
Competitiveness 8.8
ICO 8.4
Team 9.0
Community 6.5
Marketing 9.7
Documentation 9.2
Financials 6.0
Reliability 7.8
Feasibility 6.5

Idea
The mission to create a powerful video surveillance system, which is normally exorbitantly prohibitive, accessible to regular users and small business, as well as bringing public and personal security protection through video surveillance to a more advanced level, is innovative. However, it would depend on the successful wide adoption by the mass consumers and the availability of fog computer power to support the implementation of such a huge system.

Competitiveness
Despite the existence of other video surveillance solution companies, our team believes that if im…

This content is for members only.
Sign up to get the exclusive access to all our analyses and reports for FREE!

Faceter is the first blockchain surveillance system for consumers. Faceter
makes video surveillance smart, giving brains to cameras through
enhanced face detection, object detection and real-time video analysis.
These features allow cameras to understand the situation and respond
to it, oering much better security to all the customers.
Computer vision technology on the blockchain powered by a decentralized
network of miners makes the product aordable for all-sized businesses and
mass market consumers. The benefit from a mining perspective is that
a contribution of miner’s resources to Faceter is twice as profitable as the
mining of Ether on the same Graphic Processing Units (GPU’s)*. At Faceter’s
technological core is the absolute respect for privacy, and the utilization
of the features of convolutional neural networks to split the tasks reinforces
this commitment. As a result, sensitive data is always processed in a
completely trusted environment, and all images not subject to recovery
are passed to the decentralized network. This amounts up to 80% of the
total amount of calculations performed.

Team Members

Robert Pothier
CEO, Co-founder

 

Vladimir Tchernitski
Co-founder, CTO

     

Paul Scott
Business Development

 

Leon Olckers
Technical Deployments

 

Jayson Gouws
Solutions and Distribution

 

Graham Perry
Sales and Distribution

Robert Pothier
CEO, Co-founder

 

Vladimir Tchernitski
Co-founder, CTO

     

Paul Scott
Business Development

 

Leon Olckers
Technical Deployments

 

Jayson Gouws
Solutions and Distribution

 

Graham Perry
Sales and Distribution

Advisors

Igor Karavaev
Investor Relations

 

Igor Karavaev
Investor Relations

 

February 2, 2014

The birth of mankind

Something really big happened around this period of time. It affected all of humanity. That explains everything.

February 2, 2014
May 10, 2015

The birth of mankind

Something really big happened around this period of time. It affected all of humanity. That explains everything.

May 10, 2015
June 21, 2016

The birth of mankind

Something really big happened around this period of time. It affected all of humanity. That explains everything.

June 21, 2016
2014
Vladimir Chernitsky joined the team that was working to create a solution for scanning bank cards using computer vision. By that time, he had already been engaged in research and development in the field of computer vision and artificial intelligence (deep learning) for one year. The team tested all marketable products available on the market and found out that none of the open source libraries available at the time made it possible to create a simple and efficient bank card scanner. Under the supervision of the new CTO, the team developed a plan to create its own product.
2014
Q1-2 2015
The team launches its first successful product in the field of computer vision: Pay.Cards. An open source library for iOS and Android platforms allows users to embed a bank card scanner into mobile applications. The scanner is capable of recognizing not only the card number, but also the validity period and the card holder’s name. According to the test results, the product is recognized as one of the best on the market and outperforms such competitors as cards.io and Apple Pay.
Q1-2 2015
Q3-4 2015
An idea is born to apply the team’s accumulated experience to achieve a bigger goal: to create a public security system powered by face and object recognition technology. The team starts working on the project called “scanface”.
Q3-4 2015
2016
The team develops and tests a variety of neural network training algorithms and achieves high accuracy of face recognition. These developments form the basis of a product called Scanface (scanface.io). The created algorithms demonstrate high results in Megaface and LFW tests
2016
2017
The first pilot projects to test the product in real conditions in South Africa are launched. Representatives of these companies are satisfied with the test results and are ready to sign contracts to use the product for commercial purposes. The team is full of enthusiasm to make face detection and video stream analysis algorithms available to mass users.
2017