Update README.md

README.md

@@ -1,7 +1,6 @@
 # uuft.ai (facial access control)
 
-This project is a facial access control system that allows you to setup a facial biometric permission application to provide access to various applications, IO devices or other types of access control.
-The application is called `uuft` which is Ghent dialect for `head` and relates to concept the face recognition.
+This project is a facial access control system that allows you to setup a facial biometric permission application to provide access to various applications, IoT devices, IOs or other types of access control. The application is called `uuft` which is Ghent dialect for `head` and relates to concept the face recognition.
 
 This project is currently under construction, so expect some brutal changes coming up ;)
 
@@ -11,17 +10,16 @@ To be completed
 
 ## How does it work?
 
-Uuft is a web based application that allows you to build and administer the biometrics of people within you organisation that requires biometric access through face detection. The application allows you to:
+Uuft is a web based application that allows you to build and administer the biometrics of people within your organisation or group that requires biometric access through face detection. The application allows you to:
 - Create an hierarchy of user accounts
 - Collection of biometric data from specific users
 - Assign access to various applications (APIs) and or other IoT devices through the concept of plugins
 - Create custom landing pages for specific scenarios (entering a meeting room, open postbox, request access to an application, and more).
-- ...
 
 Next to the web based application, a machine learning workload is running in the background allowing to retrain or add more faces to the face recognition model on the fly, and more:
-- On the fly updating of new biometric data
+- On the fly updating of new biometric data and user accounts.
 - Isolation of biometric data on your infrastructure (you own the biometrics, nothing is in the cloud)
-- ....
+- Ability to scale through Kubernetes and the NVidia operator.
 
  ## Designs
 
@@ -31,20 +29,20 @@ You can view the designs and mockups for our project on Figma. Click the link be
 
 ## Architecture
 
-A high-level architecture is visualised below, showing how the different components in this project are communicating. The idea is that each component `Web app`, `Back-end` and `Face recognition` can be installed where your prefer. For example you could deploy the `Web app` and `Back-end` on a cloud provider, and self-host the `Face recognition` on your own private cloud or edge deployment to make sure you are owning the biometric data.
+A high-level architecture is visualised below, showing how the different components in this project are communicating. The idea is that each component `ui`, `api` and `ml` can be installed where you want. For example you could deploy the `ui` and `api` on a cloud provider, and self-host the `ml` part on your own private cloud or edge deployment to make sure you are owning the biometric data.
 
 ![Architecture](./assets/images/architecture.svg)
 
-## Web app (ui)
+## UI (front-end)
 
-The web based application will make use of the [Next.js](https://nextjs.org/) framework. [Storybook](https://storybook.js.org/)
+The front-end makes use of the [Next.js](https://nextjs.org/) framework. [Storybook](https://storybook.js.org/)
  will be used to keep track of and test our UI components.
 
-## Back-end (api)
+## API (back-end)
 
-The back-end or API is written in Golang, and defines specific methods to persist data and call the face recognition model.
+The back-end or API is written in Golang, and defines specific methods to persist data and call the face recognition model. It has various API methods to create new users, assign permissions and access control, and (re)train and interfere with the facial recognition model.
 
-## Face recognition (ml)
+## ML (face recognition)
 
 To be documented
 
@@ -62,9 +60,10 @@ You might not always require surveillance or ip cameras for your usecase. For ex
 
 A few example usecases are:
 
+- Blacklist: notify if a person was detected which is added to a blacklist.
 - Meeting room verification: validate if the user in front of the door has made a booking into the system. On validation of the person, the meeting room door unlocks.
 - Welcome desk: a kiosk screen at the entrance of a building, who can recognise your profile and shows relevant updates for you (urgent messages, next meeting, etc).
-- Locker: open a personal locker or mailbox.
+- Automated locker: open a personal locker or mailbox.
 
 As described above you might already have your own usecase, for which you would like to integrate facial recognition in. By using `uuft` you can rely on a stable and user-friendly system that scales, and do not have to build a complete facial recognition system from scratch. Using `apis` and a management pane you can quickly onboard new facial biometrics and provide access to your solution in a matter of seconds.