Create a facial recognition and liveness detection authentication system using Python.

For more details please visit : [login to view URL] Server Installation Finally the OUTPUT should be given as docker image. Facial Recognition and Liveness Detection Implementation Plan When capturing a face, we need to determine whether we should simply pass the video through an API or process the image within the Flutter app. If any processing is necessary within the app, we need to know whether you will provide the necessary resources or if we will have to develop them ourselves. Alternatively, a simple API may suffice. After successfully capturing a face, the API will return a corresponding face ID, which we will store in both our app and server. To search for a face, we will once again pass a video or photo through the API. Once processed, the API will return the corresponding face ID, which will enable us to search for and mark attendance. Criteria for acceptable images include: Do we need to check for the source of the error - whether it originates from our end or via the REST API? The face visibility should be at least 50%. The face should be positioned in such a way that at least 80% to 100% of it is perpendicular to the image. The image should be captured in natural lighting and should not have any image filters applied. Images with faces wearing goggles, scarfs or masks, helmets or any obstruction in front of the face are not acceptable. Understanding Liveliness Detection for Face Recognition Technology Whether the liveness detection should be performed on the server or locally within the app. Liveness detection is a technique used to differentiate between live individuals and manipulated photos/[login to view URL] "LiveRecognition" feature showcases how face detection and recognition can be performed in real-time using a live video feed from a camera. Active liveness detection involves tracking specific facial movements, such as eye blinks, head swivels, or smile/frown changes, to confirm the user's authenticity. Liveness detection is essential to ensure that no one can deceive the app with a false representation of a user's face. Liveness detection allows distinguishing live persons from photos and videos, ensuring that no one spoofs your app with a fake representation of a user's face. Active liveness detection allows you to ensure that a user is real by tracking eye blinks, head swivel, or smile/frown changes. Obtaining Error Messages for Liveness Detection Is the error message for liveness detection obtained through the REST API after passing the video to the server, or is liveness detected within the app itself?