@datachecker/faceverify

FaceVerify

This project contains Datachecker's FaceVerify tool, that captures images of faces to be used in liveness detection. The tool only takes a capture once the trigger mechanism is fired.

To perform liveness detection, two slightly different images of the same person are required. For example, when a person moves his/her head slightly this will generate a different image. Therefore, the tool checks difference in movement between frames.

The tool features user challenge-response, namely head pose estimation, in order to prevent video injection attacks.

The tool will be run in the browser and is therefore written in JavaScript.

Trigger mechanism

The tool performs the following checks:

• Is the environment not too dark (under exposure)?
• Is there a face?
• Is the face occluded?
• Is the detected face not too far?
• Is the detected face not too close?
• Is the face centered?
• Is the image sharp?
• Is there movement?

The movement check will only be used for the second picture. Since the first picture has no other picture to compare to.

Prerequisites

Please visit Datachecker API documentation.

• Datachecker OAuth Token
• Datachecker SDK Token

Steps

1. Request OAUTH Token
2. Put OAuth in header
3. SDK configuration (add SDK token)
4. Run SDK

OAuth Token

Datachecker uses OAuth authorization. In order to request the SDK token you will need to provide a valid OAuth token in the header.

Example header:

header = {'Authorization': `Bearer ${response.accessToken}`}

This OAuth token can be retrieved with the Datachecker OAuth Token API. The scope "productapi.sdk.read" needs to be present to make use of the SDK token. If this scope is missing you will not be able to retrieve an SDK token.

FaceVerify also requires the other following scopes to send and receive results: "productapi.faceverify.write", "productapi.poll.read", "productapi.result.read"

Example OAuth:

fetch(<BASE_ENDPOINT>+"/oauth/token", {
    method: 'POST',
    body: JSON.stringify({
        "clientId": <CLIENTID>,
        "clientSecret": <CLIENTSECRET>,
        "scopes": [
            "productapi.sdk.read",
            "productapi.faceverify.write",
            "productapi.poll.read",
            "productapi.result.read",
        ]
    })
})
.then(response => response.json())

Note: Contact Datachecker for client_id and client_secret.

SDK Token

The SDK is locked. In order to use the SDK in production a token is required. The application can only be started with a valid token. This token is a base64 string. The token can be generated by calling the Datachecker SDK Token API.

Example:

fetch(<BASE_ENDPOINT>+"/sdk/token?number_of_challenges=2&customer_reference=<CUSTOMER>&validateWatermark=true&services=FACE_VERIFY", {
    method: 'GET',
    headers: {
        'Accept': 'application/json',
        'Content-Type': 'application/json',
        'Authorization': `Bearer <ACCESSTOKEN>`
    }
})
.then(response => response.json())

Configuration

To run this tool, you will need initialise with the following variables.

ATTRIBUTE	FORMAT	DEFAULT VALUE	EXAMPLE	NOTES
ASSETS_FOLDER	string	""	"../"	optional Specifies location of locally hosted assets folder. (see Asset Fetching Configuration)
ASSETS_MODE	string	"CDN"	"LOCAL"	optional Specifies mode of asset fetching, either through CDN or locally hosted assets. (see Asset Fetching Configuration)
BACKEND	string	wasm	wasm	optional Neural network execution provider. Possible values: [wasm, webgl, cpu]. wasm is recommended whereas cpu is not recommended.
BACKGROUND_COLOR	string (Hex color code)	"#1d3461"	"#1d3461"	optional Specifies the background color using a hex color code.
CAPTURE_WAITING_TIME	int	0	500	optional Waiting time between capturing in milliseconds.
CHALLENGES	array		['up', 'right', 'down', 'left', 'up']	optional Array of challenges that can be used for Demo purposes.
CONTAINER_ID	string		"FV_mount"	required div id to mount tool on.
COUNTDOWN_MAX	int	0	500	optional If COUNTDOWN == 0 then countdown will be a random between COUNTDOWN_MIN and COUNTDOWN_MAX.
COUNTDOWN_MIN	int	0	0	optional If COUNTDOWN == 0 then countdown will be a random between COUNTDOWN_MIN and COUNTDOWN_MAX.
COUNTDOWN	int	0	3000	optional Countdown in ms before picture is taken.
DEBUG	bool	false	false	optional When debug is true more detailed logs will be visible.
DOWN_THRESHOLD	int	30	30	optional Challenge down threshold value.
LANGUAGE	string	"nl"	"nl"	required Notifications in specific language.
LEFT_THRESHOLD	int	22	22	optional Challenge left threshold value.
MODELS_PATH	string	"models/"	"models/"	optional Path referring to models location.
MOVEMENT_THRESHOLD	int	20	20	optional Movement will be calculated from frame to frame with a value between 0-100. Recommended value between 20 and 30.
RIGHT_THRESHOLD	int	22	22	optional Challenge right threshold value.
STOP_AFTER	int		10000	optional Stopping timer in ms.
TOKEN	string		see SDK Token	required Datachecker SDK token.
UP_THRESHOLD	int	35	35	optional Challenge up threshold value.
onComplete	javascript function		function(data) {console.log(data)}	required Callback function on complete .
onError	javascript function	function(error) {console.log(error)}	function(error) {console.log(error)}	required Callback function on error.
onUserExit	javascript function	function(error) {console.log(error)}	function(error) {window.history.back()}	required Callback function on user exit.

Asset fetching Configuration

FaceVerify requires fetching assets, which can be done either through a CDN or by hosting them locally. Configure this in the tool settings as follows:

CDN Configuration

// configuration
{
    ASSETS_MODE: "CDN",
    // other configurations
}

Locally Hosting Configuration

To host assets locally, first copy them to your desired location:

cp -r dist/assets/ path/to/hosted/assets/

Then, configure the tool to use these local assets:

// configuration
{
    ASSETS_MODE: "LOCAL",
    ASSETS_FOLDER: "path/to/hosted/assets/",
    // other configurations
}

For comphrehensive integration examples, please refer to our Integration Examples.

Version Control

To ensure compatibility:

• Separate Asset Versioning: The assets directory contains a version file, separate from the main file's version.
• Compatibility Check: The main file will perform a version check and throw an error if the versions are incompatible.

Handling callbacks

Within the application, you can take advantage of three callback functions to enhance the user experience and manage the flow of your process.

Note: When integrating the application into Native Apps using web views, it's essential to adapt and utilize these callback functions according to the conventions and requirements of the native platforms (e.g., iOS, Android). Native app development environments may have specific ways of handling JavaScript callbacks, and you should ensure seamless communication between the web view and the native code.

Example Web (JS):

let FV = new FaceVerify();
FV.init({
    CONTAINER_ID: 'FV_mount',
    LANGUAGE: 'en',
    TOKEN: '<SDK_TOKEN>',
    onComplete: function(data) {
        console.log(data);
    },
    onError: function(error) {
        console.log(error)
    },
    onUserExit: function(error) {
        console.log(error);
        window.history.back()
    }
});

ATTRIBUTE	FORMAT	DEFAULT VALUE	EXAMPLE	NOTES
onComplete	javascript function		function(data) {console.log(data)}	required Callback that fires when all interactive tasks in the workflow have been completed.
onError	javascript function	function(error) {console.log(error)}	function(error) {console.log(error)}	required Callback that fires when an error occurs.
onUserExit	javascript function	function(error) {console.log(error)}	function(error) {window.history.back()}	required Callback that fires when the user exits the flow without completing it.

onComplete

This callback function will be called once all the tasks within the workflow succesfully have been completed. This callback function is required. The data parameter within the function represents the output of the completed process. You can customize this function to handle and display the data as needed.

Example Web (JS):

Within the example below we are logging the output (data) to console.

let FV = new FaceVerify();
FV.init({
    ...,
    onComplete: function(data) {
        console.log(data);
        FV.stop();
    }
});

onError

This callback can be used to alert users when something goes wrong during the process. This callback function is required. The error parameter within the function contains information about the specific error encountered, allowing you to log or display error messages for debugging or user guidance. The errors that are thrown are either known or unknown. The known errors can be found within the Languages dictionary. On the other hand, the unknown errors will be thrown as is.

Example Web (JS):

Within the example below we are logging the output (error) to console.

let FV = new FaceVerify();
FV.init({
    ...,
    onError: function(error) {
        console.log(error)
    }
});

onUserExit

This callback can be used to implement actions like returning users to the previous page or prompting them for confirmation before exiting to ensure they don't lose any unsaved data or work. This callback function is required. The error parameter within the function contains information about the specific error encountered, allowing you to log or display error messages for debugging or user guidance. The error that is thrown is "exit".

Example Web (JS):

Within the example below we are logging the output (error) to console. Finally, we move back one page in the session history with window.history.back().

let FV = new FaceVerify();
FV.init({
    ...,
    onUserExit: function(error) {
        console.log(error);
        window.history.back()
    }
});

Usage/Examples

The tool first needs to be initialised to load all the models. Once its initialised, it can be started with the function FV.start();

let FV = new FaceVerify();
FV.init({
    CONTAINER_ID: ...,
    LANGUAGE: ...,
    TOKEN: ...,
    onComplete: ...,
    onError: ...,
    onUserExit: ...,
}).then(() => {
    FV.start();
});

To stop the camera and delete the container with its contents the stop function can be called. This function will automatically be called within onComplete, onError and onUserExit thus do not have to be called within your own custom versions of these functions.

...
FV.stop();

Example below:

let FV = new FaceVerify();
FV.init({
    CONTAINER_ID: 'FV_mount',
    LANGUAGE: 'nl',
    TOKEN: '<SDK_TOKEN>',
    onComplete: function(data) {
        console.log(data);
    },
    onError: function(error) {
        console.log(error)
    },
    onUserExit: function(error) {
        console.log(error);
        window.history.back();
    },
});

Importing SDK

Import the SDK with one of the three methods: Script tag, ES6 or CommonJS.

Script Tag

Easily add FaceVerify to your HTML files using the Script Tag method.

<!-- Add FaceVerify directly in your HTML -->
<script src="dist/faceverify.obf.js"></script>

NPM

For projects using NPM and a module bundler like Webpack or Rollup, you can import FaceVerify as an ES6 module or with CommonJS require syntax.

// Import FaceVerify in your JavaScript file

// ES6 style import
import FaceVerify from '@datachecker/faceverify';

// CommonJS style require
let FaceVerify = require('@datachecker/faceverify')

Demo

<!DOCTYPE html>
<html>
<head>
<title>FaceVerify</title>
</head>

<body>
    <div id="FV_mount" style="height:100vh">
    </div>
</body>

<script src="faceverify.obf.js" type="text/javascript"></script>
<script>
    let FV = new FaceVerify();
    FV.init({
        CONTAINER_ID: 'FV_mount',
        LANGUAGE: 'en',
        TOKEN: '<SDK_TOKEN>',
        onComplete: function(data) {
            console.log(data)
        },
        onError: function(error) {
            console.log(error)
        },
        onUserExit: function(error) {
            console.log(error);
            window.history.back();
        },
    });    
</script>

</html>

Languages

There are two ways in which notifications can be loaded: from file, from object (json).

File

The languages can be found in assets/language/. The current support languages are en and nl. More languages could be created.

The notifications can be loaded in configuration like the following:

let FV = new FaceVerify();
FV.init({
    LANGUAGE: 'en',
    ...

To create support for a new language, a js file needs to be created with specific keys. The keys can be derived from the current language js files (assets/language/en.js).

Example:

var LANGUAGE = {
    "start_prompt": "Tap to start",
    "no_face": "No face detected",
    "nod_head": "Please nod your head",
    "face_thresh": "Face covered",
    "face_far": "Move closer",
    "face_close": "Move back",
    "exp_dark": "Image is too dark",
    "blur": "Hold still",
    "capture_error": "Camera access is required",
    "challenge_0": "Center your face",
    "challenge_out": "Face too far",
    "challenge_1": "Look up",
    "challenge_2": "Look right",
    "challenge_3": "Look down",
    "challenge_4": "Look left",
    "tutorial": "Follow the instructions",
    "continue": "Continue"
}

Object (json)

Notifications can also be loaded as a json object like the following:

let FV = new FaceVerify();
FV.init({
    LANGUAGE: JSON.stringify(
        {
            "start_prompt": "Tap to start",
            "no_face": "No face detected",
            "nod_head": "Please nod your head",
            "face_thresh": "Face covered",
            "face_far": "Move closer",
            "face_close": "Move back",
            "exp_dark": "Image is too dark",
            "blur": "Hold still",
            "capture_error": "Camera access is required",
            "challenge_0": "Center your face",
            "challenge_out": "Face too far",
            "challenge_1": "Look up",
            "challenge_2": "Look right",
            "challenge_3": "Look down",
            "challenge_4": "Look left",
            "tutorial": "Follow the instructions",
            "continue": "Continue"
        }
    ),
    ...

Models

The tool uses a collection of neural networks. Make sure that you host the full directory so the models can be accessed. The models path can be configured. (see Configuration) The models are located under models/. Model cards can also be found in this directory.

Challenges

User challenges are implemented, in order to prevent video injection attacks. These challenges are randomly chosen and thereby, processes are different from one another. The challenges consist of head pose estimation. The performed head poses with be compared with the challenges and that result will be returned as bool in output. (see Output)

There are four poses that will be detected:

• up
• right
• down
• left

Challenges are embedded in the TOKEN. Therefore, the challenges are not directly visible.

let FV = new FaceVerify();
FV.init({
    CONTAINER_ID: 'FV_mount',
    LANGUAGE: 'nl',
    TOKEN: "<SDK_TOKEN>",
    ...

Output

The SDK will output in the following structure:

{   
    "images": [{"data":"<BASE64_IMG>", "type":"LIVE"}, "..."],
    "meta": [{"x":"", "y":"", "width":"", "height":""}, "..."],
    "token": "<SDK_TOKEN>",
    "transactionId": "<TRANSACTION_ID>",
    "valid_challenges": "true|false"
}

Example:

{   
    "images": [{"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"LIVE"}, {"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"LIVE"}, {"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"LIVE"}],
    "meta": [{"x": 33, "y": 182, "width": 265, "height": 354}, {"x": 33, "y": 182, "width": 265, "height": 354}, {"x": 33, "y": 182, "width": 265, "height": 354}],
    "token": "<SDK_TOKEN>",
    "transactionId": "<TRANSACTION_ID>",
    "valid_challenges": true
}

FaceVerify API call

If you want to send the images to the Datachecker FaceVerify API you must add a comparison image. This comparison image can either be a portrait picture from an identity card or a selfie. To add this image, you need to use type: "COMPARE".

Example JS:

let faceverify_output = {   
    "images": [{"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"LIVE"}, {"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"LIVE"}, {"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"LIVE"}],
    "meta": [{"x": 33, "y": 182, "width": 265, "height": 354}, {"x": 33, "y": 182, "width": 265, "height": 354}, {"x": 33, "y": 182, "width": 265, "height": 354}],
    "token": "<SDK_TOKEN>",
    "transactionId": "<TRANSACTION_ID>",
    "valid_challenges": true
}
let images = faceverify_output.images
let portrait_image = {"data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...", "type":"COMPARE"}

images.unshift(portrait_image)
let data = {"images": images, "transaction_id":faceverify_output.transactionId}


fetch(<BASE_ENDPOINT>+"/faceverify", {
        method: 'POST',
        headers: <HEADERS>,
        body: JSON.stringify(data)
        })
        .then(response => response.json())