Standard SDK - iOS

Face Recognition with Face Liveness Detection

Features

License

We offer lifetime license(perpetual license) based on bundle ID from iOS project. The license is available for one-time payment. In other words, once you purchase license from me, you can use our SDK permanently.

To request a license, please contact us:

Email: contact@kby-ai.com

Telegram: @kbyai

WhatsApp: +19092802609

Skype: live:.cid.66e2522354b1049b

System Requirements

  • CPU: 2 cores or more

  • RAM: 100MB or more

  • OS: iOS 13.0 or later

  • Architecture: arm64-v8a

Setup

  1. Copy the SDK (facesdk.framework folder) to the root folder of your project.

  2. Add SDK framework to the project in Xcode.

Project Navigator -> General -> Frameworks, Libraries, and Embedded Content

  1. Add the bridging header to your project settings

Project Navigator -> Build Settings -> Swift Compiler - General

Initializing SDK

  1. Step one

  • To begin, you need to activate the SDK using the license that you have received.

FaceSDK.setActivation("...")
  • If activation is successful, the return value will be SDK_SUCCESS. Otherwise, an error value will be returned.

  1. Step Two

  • After activation, call the SDK's initialization function.

FaceSDK.initSDK()
  • If initialization is successful, the return value will be SDK_SUCCESS. Otherwise, an error value will be returned.

Enums and Classes

1. SDK_ERROR

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

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.

APIs

1. setActivation

To begin, you need to activate the SDK using the license that you have received.

var ret = FaceSDK.setActivation("...")

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

2. initSDK

After activation, call the SDK's initialization function.

ret = FaceSDK.initSDK()

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

3. faceDetection

The SDK offers a single function for detecting face and liveness detection, which can be used as follows:

let faceBoxes = FaceSDK.faceDetection(image)
func captureOutput(_ output: AVCaptureOutput, didOutput sampleBuffer: CMSampleBuffer, from connection: AVCaptureConnection) { 
     
    guard let pixelBuffer: CVPixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer) else { return } 
     
    CVPixelBufferLockBaseAddress(pixelBuffer, CVPixelBufferLockFlags.readOnly) 
    let image = CIImage(cvPixelBuffer: pixelBuffer).oriented(CGImagePropertyOrientation.leftMirrored) 
    let capturedImage = UIImage(ciImage: image) 
    CVPixelBufferUnlockBaseAddress(pixelBuffer, CVPixelBufferLockFlags.readOnly) 
     
    let faceBoxes = FaceSDK.faceDetection(capturedImage) 
    DispatchQueue.main.sync { 
        self.faceView.setFrameSize(frameSize: capturedImage.size) 
        self.faceView.setFaceBoxes(faceBoxes: faceBoxes) 
    } 
} 

This function takes a single parameter, which is a UIImage object.

The return value of the function is a list of FaceBox objects.

4. templateExtraction

The FaceSDK provides a function that can generate a template from a UIImage.

This template can then be used to verify the identity of the individual captured in the image.

let templates = FaceSDK.templateExtraction(capturedImage, faceBox: faceBox)

The SDK's template extraction function takes two parameters: a UIImage object and an object of FaceBox.

The function returns Data, which contains the template that can be used for person verification.

5. similarityCalucation

The similarityCalculation function takes a Data of two templates as a parameter.

float similarity = FaceSDK.similarityCalucation(templates1, templates1);

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

Default Thresholds

let livenessThreshold = 0.7
let identifyThreshold = 0.8
  • If the liveness score exceeds 0.7, the face is a real face.

  • If the similarity between two faces is higher than 0.8, the face matching is successful.

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