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Acuant's latest SDK with most advanced image capture technology and optimized user workflow
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AcuantCamera cocoapods Apr 8, 2019
EmbeddedFrameworks v11.2 May 1, 2019
SampleApp v11.2 May 1, 2019
document_images updates Apr 26, 2019
AcuantiOSSDKV11.podspec v11.2 May 1, 2019
README.md v11.2 May 1, 2019

README.md

Acuant iOS SDK v11.2

Last updated April 2019

Copyright 2019 Acuant Inc. All rights reserved.

This document contains proprietary and confidential information and creative works owned by Acuant and its respective licensors, if any. Any use, copying, publication, distribution, display, modification, or transmission of such technology, in whole or in part, in any form or by any means, without the prior express written permission of Acuant is strictly prohibited. Except where expressly provided by Acuant in writing, possession of this information shall not be construed to confer any license or rights under any Acuant intellectual property rights, whether by estoppel, implication, or otherwise.

AssureID and i-Dentify are trademarks of Acuant Inc. Other Acuant product or service names or logos referenced this document are either trademarks or registered trademarks of Acuant.

All 3M trademarks are trademarks of Gemalto Inc.

Windows is a registered trademark of Microsoft Corporation.

Certain product, service, or company designations for companies other than Acuant may be mentioned in this document for identification purposes only. Such designations are often claimed as trademarks or service marks. In all instances where Acuant is aware of a claim, the designation appears in initial capital or all capital letters. However, you should contact the appropriate companies for more complete information regarding such designations and their registration status.

April 2019

Acuant Inc.

6080 Center Drive, Suite 850

Los Angeles, CA 90045

==================


Introduction

This document provides detailed information about the Acuant iOS SDK.


Modules

The SDK includes the following modules:

Acuant Common Library (AcuantCommon) :

  • Contains all shared internal models and supporting classes

Acuant Camera Library (AcuantCamera) :

  • Implemented using iOS native camera library
  • Uses AcuantImagePreparation for cropping

Acuant Image Preparation Library (AcuantImagePreparation) :

  • Contains all image processing such as cropping, calculation of sharpness and glare

Acuant Document Processing Library (AcuantDocumentProcessing) :

  • Contains all the methods to upload the document images, process and get results

Acuant Face Match Library (AcuantFaceMatch) :

  • Contains a method to match two face images

Acuant HG Liveness Library (AcuantHGLiveness):

  • Uses iOS native camera library to capture facial liveliness using a proprietary algorithm

Acuant IP Liveliness Library (AcuantIPLiveness):

  • Uses proprietory algorithm to detect a live person

Setup

  1. Add the following dependent embedded frameworks:
  • AcuantCommon
  • AcuantImagePreparation
  • AcuantCamera
  • AcuantDocumentProcessing
  • AcuantHGLiveness
  • AcuantIPLiveness
  • AcuantFaceMatch

  1. Create a plist file named AcuantConfig which includes the following details:

     <?xml version="1.0" encoding="UTF-8"?>
     <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
     <plist version="1.0">
     	<dict>
     		<key>acuant_username</key>
     		<string>xxxxxx@acuantcorp.com</string>
     		<key>acuant_password</key>
     		<string>xxxxxxxxxx</string>
     		<key>acuant_subscription</key>
     		<string>xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx</string>
     		<key>frm_endpoint</key>
     		<string>https://frm.acuant.net</string>
     		<key>med_endpoint</key>
     		<string>https://medicscan.acuant.net</string>
     		<key>assureid_endpoint</key>
     		<string>https://services.assureid.net</string>
     	</dict>
     </plist>
    

Using COCOAPODS

  1. If you are using COCOAPODS, then add the following podfile:

     platform :ios, '11.0'
     pod 'AcuantiOSSDKV11', '~> 11.2.0.1'
     pod 'iProov', :git => 'https://github.com/iProov/ios.git', :tag => '6.3.0'
    
  2. Make sure you have added the AcuantConfig.plist file to the project.


Capture an Image using AcuantCamera :###

Open the camera:

let documentCameraController = DocumentCameraController.getCameraController(delegate:self,
captureWaitTime:captureWaitTime)

AppDelegate.navigationController?.pushViewController(documentCameraController, animated: false)

Get the captured image:

public protocol CameraCaptureDelegate {
	func setCapturedImage(image:Image, barcodeString:String?)
}

Note: AcuantCamera is depdendent on AcuantImagePreparation and AcuantCommon.


AcuantImagePreparation

This module contains all image preparation functionality.

  • Initialization

    AcuantImagePreparation.initialize(delegate: InitializationDelegate)
    
    public protocol InitializationDelegate {
    	func initializationFinished(error: AcuantError?);
    }
    
  • Crop

After the image is captured, it is sent to the cropping library for cropping.

	public class func crop(options: CroppingOptions, data: CroppingData)->Image

	// CroppingOptions, and CroppingData & Image are part of AcuantCommon
	// Sample

	let croppingData  = CroppingData()
    croppingData.image = image // UIImage


    let croppingOptions = CroppingOptions()
    croppingOptions.isHealthCard = false

    let croppedImage = AcuantImagePreparation.crop(options: croppingOptions, data: croppingData)
  • Sharpness

This method returns a sharpness value of an image. If sharpness value is greater than 50, then the image is considered sharp (not blurry).

	public class func sharpness(image: UIImage)->Int
  • Glare

This method returns glare value of an image. If glare value is greater than 50, then the image does not have glare.

	public class func glare(image: UIImage)->Int

AcuantDocumentProcessing

After a document image is captured, it can be processed using the following steps.

Note: If an upload fails with an error, retry the image upload using a better image.

  1. Create an instance:

     public class func createInstance(processingMode:ProcessingMode,options:IdOptions,delegate:CreateInstanceDelegate)
    
     public protocol CreateInstanceDelegate{
     	func instanceCreated(instanceId : String?,error:AcuantError?);
     }
    
  2. Upload an image:

     public class func uploadImage(processingMode:ProcessingMode,instancdId:String,data:IdData,options:IdOptions,delegate:UploadImageDelegate)
    
     public protocol UploadImageDelegate{
     	func imageUploaded(error: AcuantError?,classification:Classification?);
     }
    
  3. Get the data:

     public class func getData(instanceId:String,isHealthCard:Bool,delegate:GetDataDelegate?)
    
     public protocol UploadImageDelegate{
     	func imageUploaded(error: AcuantError?,classification:Classification?);
     }
    
  4. Delete the instance:

     public class func deleteInstance(instanceId : String,type:DeleteType, delegate:DeleteDelegate)
    
     public protocol DeleteDelegate {
     	func instanceDeleted(success : Bool)
     }
    

AcuantHGLiveness

This module checks for liveness (whether the subject is a live person) by using blink detection. The user interface code for this is contained in the Sample application (FaceLivenessCameraController.swift) which customers may modify for their specific requirements.

Create a face live capture session

	public class func getFaceCaptureSession(delegate:AcuantHGLiveFaceCaptureDelegate?,captureDevice: AVCaptureDevice?,previewSize:CGSize?)->FaceCaptureSession

	public protocol AcuantHGLiveFaceCaptureDelegate {
		func liveFaceDetailsCaptured(liveFaceDetails: LiveFaceDetails?)
	}

Example

	self.captureSession = AcuantHGLiveness.getFaceCaptureSession(delegate: self,captureDevice: captureDevice,previewSize:self.view.layer.bounds.size)

	// Code for HG Live controller
	let liveFaceViewController = FaceLivenessCameraController()
	liveFaceViewController.delegate = self
	AppDelegate.navigationController?.pushViewController(liveFaceViewController, animated: true)

AcuantIPLiveness

This module checks for liveness (whether the subject is a live person):

public class func showLiveFaceCaptureInterface(del : LivenessTestDelegate )

public protocol LivenessTestDelegate {
	func livenessSetupdone()      // Called back when internal set up is done to start the liveness test UI
	func livenessTestdone()       // Called hen liveness test is done and the the download process to get the facial image has started
	func livenessTestSucceeded(image:UIImage?)  // Called in case of liveness test passed. image has the downloaded facial image
	func livenessTestFailed(error:AcuantError)  // Called in case of liveness test failed
}

Following is list of dependencies:

  • Alamofire.framework
  • AlamofireImage.framework
  • GPUImage.framework
  • iProov.framework
  • KeychainAccess.framework
  • MBProgressHUD.framework
  • SocketIO.framework

AcuantFaceMatch

This module is used to match two facial images:

	public class func processFacialMatch(facialData : FacialMatchData, delegate : FacialMatchDelegate?)

	public protocol FacialMatchDelegate {
		func facialMatchFinished(result:FacialMatchResult?)
	}

	public class FacialMatchData{
		public var faceImageOne : UIImage? = nil // Facial image from ID Card
		public var faceImageTwo : UIImage? = nil // Facial image from selfie capture during liveness check (image gets compressed by 50%)

	}

Error codes

public struct AcuantErrorCodes{
	public static let ERROR_InvalidCredentials = -1
	public static let ERROR_InvalidLicenseKey = -2
	public static let ERROR_InvalidEndpoint = -3
	public static let ERROR_InitializationNotFinished = -4
	public static let ERROR_Network = -5
	public static let ERROR_InvalidJson = -6
	public static let ERROR_CouldNotCrop = -7
	public static let ERROR_NotEnoughMemory = -8
	public static let ERROR_BarcodeCaptureFailed = -9
	public static let ERROR_BarcodeCaptureTimedOut = -10
	public static let ERROR_BarcodeCaptureNotAuthorized = -11
	public static let ERROR_LiveFaceCaptureNotAuthorized = -12
	public static let ERROR_CouldNotCreateConnectInstance = -13
	public static let ERROR_CouldNotUploadConnectImage = -14
	public static let ERROR_CouldNotUploadConnectBarcode = -15
	public static let ERROR_CouldNotGetConnectData = -16
	public static let ERROR_CouldNotProcessFacialMatch = -17
	public static let ERROR_CardWidthNotSet = -18
	public static let ERROR_CouldNotGetHealthCardData = -19
	public static let ERROR_CouldNotClassifyDocument = -20
	public static let ERROR_LowResolutionImage = -21
	public static let ERROR_BlurryImage = -22
	public static let ERROR_ImageWithGlare = -23
	public static let ERROR_CouldNotGetIPLivenessToken = -24
	public static let ERROR_NotALiveFace = -25
	public static let ERROR_CouldNotAccessLivenessData = -26
}

Error descriptions

public struct AcuantErrorDescriptions {
	public static let ERROR_DESC_InvalidCredentials = "Invalid credentials"
	public static let ERROR_DESC_InvalidLicenseKey = "Invalid License Key"
	public static let ERROR_DESC_InvalidEndpoint = "Invalid endpoint"
	public static let ERROR_DESC_Network = "Network problem"
	public static let ERROR_DESC_InitializationNotFinished = "Initialization not finished"
	public static let ERROR_DESC_InvalidJson = "Invalid Json response"
	public static let ERROR_DESC_CouldNotCrop = "Could not crop image"
	public static let ERROR_DESC_BarcodeCaptureFailed = "Barcode capture failed"
	public static let ERROR_DESC_BarcodeCaptureTimedOut = "Barcode capture timed out"
	public static let ERROR_DESC_BarcodeCaptureNotAuthorized = "Barcode capture is not authorized"
	public static let ERROR_DESC_LiveFaceCaptureNotAuthorized = "Live face capture is not authorized"
	public static let ERROR_DESC_CouldNotCreateConnectInstance = "Could not create connect Instance"
	public static let ERROR_DESC_CouldNotUploadConnectImage = "Could not upload image to connect instance"
	public static let ERROR_DESC_CouldNotUploadConnectBarcode = "Could not upload barcode to connect instance"
	public static let ERROR_DESC_CouldNotGetConnectData = "Could not get connect image data"
	public static let ERROR_DESC_CardWidthNotSet = "Card width not set"
	public static let ERROR_DESC_CouldNotGetHealthCardData = "Could not get health card data"
	public static let ERROR_DESC_CouldNotClassifyDocument = "Could not classify document"
	public static let ERROR_DESC_LowResolutionImage = "Low resolution image"
	public static let ERROR_DESC_BlurryImage = "Blurry image"
	public static let ERROR_DESC_ImageWithGlare = "Image has glare"
	public static let ERROR_DESC_CouldNotGetIPLivenessToken = "Could not get face liveness token"
	public static let ERROR_DESC_NotALiveFace = "Not a live face"
	public static let ERROR_DESC_CouldNotAccessLivenessData = "Could not access liveness data"
}

Image

public class Image {
	public var image : UIImage? = nil
	public var dpi : Int = 0 // dpi value of the captured image
	public var error : AcuantError? = nil
	public var isCorrectAspectRatio = false // If the captured image has the correct aspect ratiopublic var aspectRatio : Float = 0.0 // Aspect ratio of the captured image
	public init(){}
}

Frequently Asked Questions

What causes an "Unsupported Architecture" error when publishing the app in the Apple App store?

All frameworks are �fat� (multi-architecture) binaries that contain slices for armv7, arm64, i386, and x86(64) CPU architectures. ARM slices are used by physical iOS devices, while i386 and x86(64) are used by the simulator.

Use the lipo command to check which slices are contained in the binaries:

	lipo -info <path to the file>

You can also use the lipo command to remove unwanted slices:

	lipo -remove i386 <Path to the file> -o <Output file path>

	lipo -remove x86_64 <Path to the file> -o <Output file path>
You can’t perform that action at this time.