Liveness CameraX

An easy way to consume results from CameraX and Firebase Face Detection as an API. With simple steps, you can challenge your user before sending pictures to your services.

You also can download a sample to test from PlayStore

Installation

Set up your root build.gradle (jitpack.io):

allprojects {  
    repositories {  
        ...  
        maven { url 'https://jitpack.io' }  
    }  
}

Add dependency to your project build.gradle:

dependencies {
	implementation 'com.github.arturschaefer:liveness-camerax-android:TAG'
}

Start to use

You can check a sample of using it inside MainActivity .

You can start and listen the result passing its Context and a callback listener.

class MainActivity : AppCompatActivity() {
    private lateinit var binding: ActivityMainBinding

    override fun onCreate(savedInstanceState: Bundle?) {
        ...
        binding.btnLiveness.setOnClickListener {
            LivenessEntryPoint.startLiveness(context = this) { result ->
                result.createdByUser?.let { photoResult ->
                    // Picture taken by the user
                }
                result.createdBySteps?.let { listOfPhotos ->
                    // List of photos as result (photos from steps + the user picture)
                }
                result.error?.let { error ->
                    // Handle errors
                }
            }
        }
    }
}

Customization

You can customize all the assets and dimensions. It's only required to create the resources with the same keys in your project. It's possible to change all colors, margins, dimensions, camera button and strings. Look inside values and drawables

Challenges to request to your user

There are several steps to challenge the user, and you can check it inside StepLiveness . They are:

STEP_LUMINOSITY: Luminosity captured in real-time. We get the number of white pixels in every frame captured to define the luminosity.
STEP_SMILE: We consider a smiling probability to pass it. Some libraries called it happiness probability too.
STEP_BLINK: After both eyes opened, we checked if they blinked.
STEP_HEAD_FRONTAL, STEP_HEAD_LEFT, STEP_HEAD_RIGHT: Each step references the head's position inside an Euler's graphic. These heads steps could be confusing to the user.

How does its work

We have a preset list of steps to configure our liveness. After the user completes each step, a picture will be saved automatically on the device. When the user passes all the challenges we will show a button on the UI. It will allow him to take the last picture and finalize the process. After the user finalizes the process you'll receive as a result the picture taken by the user and a list of files automatically created. Look at the result model LivenessCameraXResult

Common examples

In scenarios when a selfie is required to improve security, we can use this library to check the user's liveness before taking the picture. It's a way to avoid several frauds and check pictures integrity before being validated on the server side, reducing costs and saving our time. The bests examples to try this library are SignUp, device authorization, or even password reset.

Why it is built like that

With this kind of result, which is a list of photos in Base64, you can choose what picture you want to show to your user on UI as a result. I recommend that he takes for this. The most common reason to do this is to require confirmation before send to your backend. With the list of files automatically taken you can compare all photos and validate if he is the same person in all of those photos.

Constraints

We have a constraint about the number of faces recognized at the same time. It's only possible to proceed with a single face recognized. In the happiest scenario, your user shouldn't have any problem. It is important to know about the worst-case scenario. When a second face is recognized we delete all the completed steps and start over. It means the completed steps and taken pictures will be canceled/removed, and your user needs to pass through the challenges again. Challenges using head movement could be tricky to the Firebase Face Detection.

Before putting this library, or any library, you need to think about accessibility. For example, if your user has a problem with his eyes, you should consider not using the blink challenge. Also, some people lose their facial movements, and in cases like that, you need to think if challenges like a smile or head movements are the best options.

Libary tech stuff

It's simple like that to use. For more technical information you can check the README.md inside the library.

Contributing

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us. Before submit any Pull Request, check if the pre-commit hook is installed in your IDE. We use some actions to validate it using detekt .

Authors

See also the list of contributors who participated in this project.

License

This project is licensed under the Apache License 2.0 - see the LICENSE.md file for details

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