Face Recognition SDK - Server

This stands for face recognition docker, facial recognition docker, face matching docker, face comparison docker, face search engine docker, face identification docker, face search ID docker on server

We implemented face recognition SDK on docker container

We provide the Face Recognition SDK for both Windows and Linux.

Features

• Face Detection

• Face Recognition

• Pose Estimation

• 68 points Face Landmark Detection

• Face Quality Calculation

• Face Occlusion Detection

• Eye Closure Detection

• Mouth Opening Check

License

We offer lifetime licenses(perpetual) based on machine ID for Servers (Windows, Linux).

To request a license, please contact us:

Email: [e-mail]

Telegram: @kbyai

WhatsApp: [phone]

Skype: live:.cid.66e2522354b1049b

System Requirements

1. Windows

• 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: Windows 7 or later

• Architecture: x64

• Dependency: OpenVINO™ Runtime (Version: 2022.3), ncnn Runtime(20220721), Vulkan SDK Runtime(1.3.250)

2. Linux

• 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

• Architecture: x64

• Dependency: OpenVINO™ Runtime (Version: 2022.3)

Import SDK

1. Python

from facesdk import getMachineCode
from facesdk import setActivation
from facesdk import initSDK
from facesdk import faceDetection
from facesdk import templateExtraction
from facesdk import similarityCalculation
from facebox import FaceBox

1. C++

#include "facesdk.h"

# CMake for Windows
target_link_libraries(your_app_name
    /path/to/library/facesdk2.lib
)

# CMake for Linux
target_link_libraries(your_app_name
    /path/to/library/libfacesdk2.so
)

Initializing SDK

1. Step one

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

# Python code example

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

// C++ code example

printf("machine code: %s\n", getMachineCode());

1. Step Two

• Next, activate the SDK using the received license.

# Python code example

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

// C++ code example

int ret = setActivation("...");

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

1. Step Three

• After activation, call the initialization function of the SDK.

# Python code example

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

// C++ code example

ret = initSDK("data");

• 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.

Enums and Structure

1. 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

2. FaceBox

This class represents the output of the face detection function that contains the detected face rectangle, liveness score, and facial angles such as yaw, roll, and pitch.

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

Face angles

68 points facial landmark

APIs

1. getMachineCode

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

# Python code example

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

// C++ code example

printf("machine code: %s\n", getMachineCode());

2. setActivation

Next, activate the SDK using the received license.

# Python code example

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

// C++ code example

int ret = setActivation("...");

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

3. initSDK

After activation, call the initialization function of the SDK.

# Python code example

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

// C++ code example

ret = initSDK("data");

The first parameter is the path to the model.

When using Windows, it is necessary to provide the complete file path for the model.

Example:

model_path = "C:\path\to\model"

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

4. faceDetection

The Face SDK provides a single API for detecting faces, performing liveness detection, 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:

# Python code example

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

// C++ code example

FaceBox* faceBoxes = (FaceBox*)malloc(sizeof(FaceBox) * maxFaceCount);
memset(faceBoxes, 0, sizeof(FaceBox) * maxFaceCount);

ret = faceDetection(image.data, image.cols, image.rows, 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.

5. templateExtraction

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:

# Python code example
faceBoxes = (FaceBox * maxFaceCount)()

...

templateExtraction(image_np1, image_np1.shape[1], image_np1.shape[0], faceBoxes[0])

// C++ code example

FaceBox* faceBoxes = (FaceBox*)malloc(sizeof(FaceBox) * maxFaceCount);
memset(faceBoxes, 0, sizeof(FaceBox) * maxFaceCount);

...

templateExtraction(image.data, image.cols, image.rows, faceBoxes[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.

6. similarityCalculation

The similarityCalculation function takes a byte array of two templates as a parameter.

# Python code example

similarity = similarityCalculation(faceBoxes1[0].templates, faceBoxes2[0].templates)

// C++ code example

FaceBox* faceBoxes = (FaceBox*)malloc(sizeof(FaceBox) * maxFaceCount);
memset(faceBoxes, 0, sizeof(FaceBox) * maxFaceCount);

...

float similarity = similarityCalculation(faceBoxes[0].templates, faceBoxes[1].templates);

It returns the similarity value between the two templates, which can be used to determine the level of likeness between the two individuals.

Default Thresholds

verifyThreshold = 0.7 

• If the similarity value between the two templates is high, it indicates that the faces are the same.