/FaceRecognition-Docker

This is the docker project for face recognition

Primary LanguagePythonOtherNOASSERTION

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đź‘Ź We have published the Face Livness Detection SDK for the server.

FaceRecognition-Docker

This project demonstrates an advanced face recognition technology implemented via a Dockerized Flask API.

It includes features that allow for testing face recognition between two images using both image files and base64-encoded images.

The demo is integrated with KBY-AI's Face Recognition Server SDK.

We can customize the SDK to align with your specific requirements.

Face Liveness Detection Face Recognition
Face Detection Face Detection
Face Liveness Detection Face Recognition
Pose Estimation Pose Estimation
68 points Face Landmark Detection 68 points Face Landmark Detection
Face Quality Calculation Face Occlusion Detection
Face Occlusion Detection Face Occlusion Detection
Eye Closure Detection Eye Closure Detection
Mouth Opening Check Mouth Opening Check

Reference

Try the API

Website

You can test the SDK using images from the following URL: http://18.221.33.238:9000/

Postman

To test the API, you can use Postman. Here are the endpoints for testing:

SDK License

This project uses KBY-AI's Face Recognition Server SDK, which requires a license per machine.

How to run

1. System Requirements

  • CPU: 2 cores or more (Recommended: 2 cores)
  • RAM: 4 GB or more (Recommended: 8 GB)
  • HDD: 4 GB or more (Recommended: 8 GB)
  • OS: Ubuntu 20.04 or later
  • Dependency: OpenVINO™ Runtime (Version: 2022.3)

2. Setup and Test

  • Clone the project:

    https://github.com/kby-ai/FaceRecognition-Docker.git

  • Download the model from Google Drive: click here

    cd FaceLivenessDetection-Docker

wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=19vA7ZOlo19BcW8v4iCoCGahUEbgKCo48' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=19vA7ZOlo19BcW8v4iCoCGahUEbgKCo48" -O data.zip && rm -rf /tmp/cookies.txt

unzip data.zip

  • Build the Docker image:

    sudo docker build --pull --rm -f Dockerfile -t kby-ai-face:latest .

  • Run the Docker container:

    sudo docker run -v ./license.txt:/home/openvino/kby-ai-face/license.txt -p 8081:8080 kby-ai-face

  • Send us the machine code and replace the license.txt file you received. Then, run the Docker container again.

    image

    image

  • To test the API, you can use Postman. Here are the endpoints for testing:

    Test with an image file: Send a POST request to http://{xx.xx.xx.xx}:8081/compare_face

    Test with a base64-encoded image: Send a POST request to http://{xx.xx.xx.xx}:8080/compare_face_base64

    You can download the Postman collection to easily access and use these endpoints. click here

About SDK

1. Initializing the SDK

  • Step One

    First, obtain the machine code for activation and request a license based on the machine code.

    machineCode = getMachineCode()
print("machineCode: ", machineCode.decode('utf-8'))

  • Step Two

    Next, activate the SDK using the received license.

    setActivation(license.encode('utf-8'))

    If activation is successful, the return value will be SDK_SUCCESS. Otherwise, an error value will be returned.

  • Step Three

    After activation, call the initialization function of the SDK.

    initSDK("data".encode('utf-8'))

    The first parameter is the path to the model.

    If initialization is successful, the return value will be SDK_SUCCESS. Otherwise, an error value will be returned.

2. Enum and Structure

  • SDK_ERROR

    This enumeration represents the return value of the 'initSDK' and 'setActivation' functions.

    Feature Value Name
    Successful activation or initialization 0 SDK_SUCCESS
    License key error -1 SDK_LICENSE_KEY_ERROR
    AppID error (Not used in Server SDK) -2 SDK_LICENSE_APPID_ERROR
    License expiration -3 SDK_LICENSE_EXPIRED
    Not activated -4 SDK_NO_ACTIVATED
    Failed to initialize SDK -5 SDK_INIT_ERROR

  • FaceBox

    This structure represents the output of the face detection function.

    Feature Type Name
    Face rectangle int x1, y1, x2, y2
    Face angles (-45 ~ 45) float yaw, roll, pitch
    Face quality (0 ~ 1) float face_quality
    Face luminance (0 ~ 255) float face_luminance
    Eye distance (pixels) float eye_dist
    Eye closure (0 ~ 1) float left_eye_closed, right_eye_closed
    Face occlusion (0 ~ 1) float face_occlusion
    Mouth opening (0 ~ 1) float mouth_opened
    68 points facial landmark float [68 * 2] landmarks_68
    Face templates unsigned char [2048] templates

    68 points facial landmark

3. APIs

  • Face Detection

    The Face SDK provides a single API for detecting faces, determining face orientation (yaw, roll, pitch), assessing face quality, detecting facial occlusion, eye closure, mouth opening, and identifying facial landmarks.

    The function can be used as follows:

    faceBoxes = (FaceBox * maxFaceCount)()
faceCount = faceDetection(image_np, image_np.shape[1], image_np.shape[0], faceBoxes, maxFaceCount)

    This function requires 5 parameters.

    • The first parameter: the byte array of the RGB image buffer.
    • The second parameter: the width of the image.
    • The third parameter: the height of the image.
    • The fourth parameter: the 'FaceBox' array allocated with 'maxFaceCount' for storing the detected faces.
    • The fifth parameter: the count allocated for the maximum 'FaceBox' objects.

    The function returns the count of the detected face.