SimFace

An Android library for face recognition and quality assessment on edge devices. It provides face detection, embedding creation, and biometric matching capabilities. The library is built around Google's MLKit for face detection and EdgeFace for embedding (template) creation. It consists of the following modules:

1. Face Detection and Quality Assessment: This module is used to locate faces within an image and assess their quality.
2. Embedding Creation: This module is used to generate a 512-float vector representation of face images.
3. Matching and Identification: Used for verification and identification purposes.

Include the library into the project.

Option 1 (Recommended)

1. Add the repository to your settings.gradle.kts under dependencyResolutionManagement under respositories:

maven {
    url = uri("https://maven.pkg.github.com/Simprints/Biometrics-SimFace")
    credentials {
        username = System.getenv("USERNAME") ?: ""
        password = System.getenv("TOKEN") ?: ""
    }
}

2. Import the dependencies in build.gradle.kts:

implementation("com.simprints.biometrics:simface:2025.2.0")

Implement the functionality.

Coroutines (Recommended)

// Initialize library configuration
val simFace = SimFace()
val simFaceConfig = SimFaceConfig(context)
simFace.initialize(simFaceConfig)

// Load a bitmap image for processing
val faceImage: Bitmap =
    BitmapFactory.decodeResource(context.resources, R.drawable.royalty_free_good_face)

lifecycleScope.launch {
    val faces = simFace.detectFaceBlocking(faceImage)
    val face = faces[0]
    if (faces.size != 1 || face.quality < 0.6) throw Exception("Quality not sufficient")

    // Align and crop the image of the face
    val alignedFace = face.alignedFaceImage(bitmap)

    // Generate an embedding from the image
    val probe = simFace.getFaceDetectionProcessor().getEmbedding(alignedFace)

    // Verify the embedding against itself
    val score = simFace.verificationScore(probe, probe)
}

Callbacks

// Initialize library configuration
val simFace = SimFace()
val simFaceConfig = SimFaceConfig(context)
simFace.initialize(simFaceConfig)

// Load a bitmap image for processing
val faceImage: Bitmap = BitmapFactory.decodeResource(context.resources, R.drawable.royalty_free_good_face)

// Note that this can be better handled with callbacks or coroutines
simFace.getFaceDetectionProcessor().detectFace(faceImage, onSuccess = { faces ->
    val face = faces[0]
    if (faces.size != 1 || face.quality < 0.6) throw Exception("Quality not sufficient")

    // Align and crop the image of the face
    val alignedFace = face.alignedFaceImage(bitmap)

    // Generate an embedding from the image
    val probe = simFace.getEmbedding(alignedFace)

    // Verify the embedding against itself
    val score = simFace.verificationScore(probe, probe)
})

Workflow

Face Detection and Embedding

We first initialize the library. Then we can use the simFace.detectFace method to detect faces in images and evaluate their quality. We can repeat this process multiple times until a sufficiently good face image is selected.

Afterwards, we can use the simFace.getEmbedding method to obtain a vector template from the selected image. The embedding is represented by a 512 float array.

Verification and Identification

The same steps are taken to initialize the library.

Then, the matching of two templates is carried out using the simFace.verificationScore method, which returns a score in the [0, 1] range, being 1 a perfect match.

Identification can be carried using the simFace.identificationScore method which returns a mapping of the referenceVectors to the the score with respect to the probe.

Both methods use the cosine similarity between vectors as a measure of the score.

System Requirements

The library works with a minimum version of Android 6.0 (API Level 23). It has been tested and runs smoothly on Samsung Galaxy A03 Core which has the following specifications:

• Android 11
• 1.6GHz Octa-core
• 2GB RAM
• 8MP f/2.0 Camera
• 32GB Storage