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👏 We have published the Face Liveness Detection, Face Recognition SDK and ID Card Recognition SDK for the server.

FaceRecognition-Docker

Overview

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

For other solutions, please explore the following:

Face Liveness Detection - Android(Basic SDK)

Face Liveness Detection - iOS(Basic SDK)

Face Recognition - Android(Standard SDK)

Face Recognition - iOS(Standard SDK)

Face Recognition - Flutter(Standard SDK)

Face Recognition - React-Native(Standard SDK)

Face Attribute - Android(Premium SDK)

Face Attribute - iOS(Premium SDK)

Try the API

Online Demo

You can test the SDK using images from the following URL: https://web.kby-ai.com

Postman

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://18.221.33.238:8081/compare_face
Test with a base64-encoded image: Send a POST request to http://18.221.33.238:8081/compare_face_base64

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

SDK License

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

The code below shows how to use the license: https://github.com/kby-ai/FaceRecognition-Docker/blob/5c6bdaff0e8154d6c6472ac9faf9158c6a6e7b47/app.py#L26-L36
In order to request the license, please provide us with the machine code obtained from the "getMachineCode" function.

Please contact us:
```
Email: contact@kby-ai.com

Telegram: @kbyai

WhatsApp: +19092802609

Skype: live:.cid.66e2522354b1049b
```

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:

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

Download the model from Google Drive: click here

cd FaceRecognition-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.
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}:8081/compare_face_base64

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

3. Execute the Gradio demo

Setup Gradio Ensure that you have the necessary dependencies installed.

Gradio requires Python 3.6 or above.

You can install Gradio using pip by running the following command:
```
pip install gradio
```
Run the demo Run it using the following command:
```
cd gradio
python demo.py
```
You can test within the following URL:
http://127.0.0.1:9000

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.
Create Template

The SDK provides a function that enables the generation of templates from RGB data. These templates can be used for face verification between two faces.

The function can be used as follows:
```
templateExtraction(image_np1, image_np1.shape[1], image_np1.shape[0], faceBoxes1[0])
```
This function requires 4 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' object obtained from the 'faceDetection' function.
If the template extraction is successful, the function will return 0. Otherwise, it will return -1.
Calculation similiarity

The 'similarityCalculation' function takes a byte array of two templates as a parameter.
```
similarity = similarityCalculation(faceBoxes1[0].templates, faceBoxes2[0].templates)
```
It returns the similarity value between the two templates, which can be used to determine the level of likeness between the two individuals.

4. Thresholds

The default thresholds are as the following below: https://github.com/kby-ai/FaceRecognition-Docker/blob/75800590cd9f2a3b778ec176bf465d1a731278fa/app.py#L18-L20

PankajFB/FaceRecognition-Docker