In this section, we listed the guides for the face matching checks.

Please note that the Oz Liveness Mobile SDK does not include a user interface for scanning official documents. You may need to explore alternative SDKs that offer that functionality or implement it on your own. Web SDK does include a simple photo ID capture screen.

This guide describes the steps needed to add face matching to your liveness check.

By this time you should have already implemented liveness video recording and liveness check. If not, please refer to these guides:

• Integration of Oz Liveness Web SDK

• Integration of Oz Liveness Mobile SDK

Adding Photo ID Capture Step to Web SDK

Simply add photo_id_front to the list of actions for the plugin, e.g.,

OzLiveness.open({
  lang: 'en',
  action: [
    'photo_id_front', 
    'video_selfie_blank'
  ],
  ...
});

Adding Face Matching to Android SDK

For the purpose of this guide, it is assumed that your reference photo (e.g., front side of an ID) is stored on the device as reference.jpg.

Modify the code that runs the analysis as follows:

private fun analyzeMedia(mediaList: List<OzAbstractMedia>) {

    val refFile = File(context.filesDir, "reference.jpg")
    val refMedia = OzAbstractMedia.OzDocumentPhoto(
        OzMediaTag.PhotoIdFront , // OzMediaTag.PhotoSelfie for a non-ID photo
        refFile.absolutePath
    )

    AnalysisRequest.Builder()
        .addAnalysis(Analysis(Analysis.Type.QUALITY, Analysis.Mode.SERVER_BASED, mediaList))
        .addAnalysis(Analysis(Analysis.Type.BIOMETRY, Analysis.Mode.SERVER_BASED, mediaList + refMedia))
        .build()
        .run(object : AnalysisRequest.AnalysisListener {
            override fun onSuccess(result: List<OzAnalysisResult>) {
                result.forEach { 
                    println(it.resolution.name)
                    println(it.folderId)
                }
            }
            override fun onError(error: OzException) {
                error.printStackTrace()
            }
        })
} 

private void analyzeMedia(List<OzAbstractMedia> mediaList) {
    File refFile = new File(context.getFilesDir(), "reference.jpg");
    OzAbstractMedia refMedia = new OzAbstractMedia.OzDocumentPhoto(
            OzMediaTag.PhotoIdFront , // OzMediaTag.PhotoSelfie for a non-ID photo
            refFile.getAbsolutePath()
    );
    ArrayList<OzAbstractMedia> mediaWithReferencePhoto = new ArrayList<>(mediaList);
    mediaWithReferencePhoto.add(refMedia);
    new AnalysisRequest.Builder()
            .addAnalysis(new Analysis(Analysis.Type.QUALITY, Analysis.Mode.SERVER_BASED, mediaList, Collections.emptyMap()))
            .addAnalysis(new Analysis(Analysis.Type.BIOMETRY, Analysis.Mode.SERVER_BASED, mediaWithReferencePhoto, Collections.emptyMap()))
            .build()
            .run(new AnalysisRequest.AnalysisListener() {
                @Override public void onStatusChange(@NonNull AnalysisRequest.AnalysisStatus analysisStatus) {}
                @Override
                public void onSuccess(@NonNull List<OzAnalysisResult> list) {
                    String folderId = list.get(0).getFolderId();
                }
                @Override
                public void onError(@NonNull OzException e) { e.printStackTrace(); }
    });
}

For on-device analyses, you can change the analysis mode from Analysis.Mode.SERVER_BASED to Analysis.Mode.ON_DEVICE

Check also the Android sample app source code.

Adding Face Matching to iOS SDK

For the purpose of this guide, it is assumed that your reference photo (e.g., front side of an ID) is stored on the device as reference.jpg.

Modify the code that runs the analysis as follows:

let imageURL = URL(fileURLWithPath: NSTemporaryDirectory())
    .appendingPathComponent("reference.jpg")

let refMedia = OZMedia.init(movement: .selfie,
                   mediaType: .movement,
                   metaData: nil,
                   videoURL: nil,
                   bestShotURL: imageUrl,
                   preferredMediaURL: nil,
                   timestamp: Date())
   
var mediaBiometry = [OZMedia]()
mediaBiometry.append(refMedia)
mediaBiometry.append(contentsOf: mediaToAnalyze)
let analysisRequest = AnalysisRequestBuilder()
let analysisBiometry = Analysis.init(media: mediaBiometry, type: .biometry, mode: .serverBased)
let analysisQuality = Analysis.init(media: mediaToAnalyze, type: .quality, mode: .serverBased)
analysisRequest.addAnalysis(analysisBiometry)
analysisRequest.addAnalysis(analysisQuality)
analysisRequest.uploadMedia(mediaBiometry)
analysisRequest.run(
    scenarioStateHandler: { state in }, // scenario steps progress handler
    uploadProgressHandler: { (progress) in } // file upload progress handler
) { (analysisResults : [OzAnalysisResult], error) in
    // receive and handle analyses results here
    for result in analysisResults {
        print(result.resolution)
        print(result.folderID)
    }
}

For on-device analyses, you can change the analysis mode from mode: .serverBased to mode: .onDevice

Check also the iOS sample app source code.

Final notes for all SDKs

You will be able to access your media and analysis results in Web UI via browser or programmatically via API.

Oz API methods as well as Mobile and Web SDK methods can be combined with great flexibility. Explore the options available in the Developer Guide section.

This guide describes how to match a liveness video with a reference photo of a person that is already stored in your database.

However, if you prefer to include a photo ID capture step to your liveness process instead of using a stored photo, then you can refer to another guide in this section.

By this time you should have already implemented liveness video recording and liveness check. If not, please refer to these guides:

• Integration of Oz Liveness Web SDK

• Integration of Oz Liveness Mobile SDK

In this scenario, you upload your reference image to the same folder where you have a liveness video, initiate the BIOMETRY analysis, and poll for the results.

1. Get folder_id

Given that you already have the liveness video recorded and uploaded, you will be working with the same Oz API folder where your liveness video is. Obtain the folder ID as described below, and pass it to your back end.

• For a video recorded by Web SDK, get the folder_id as described here.

• For a video recorded by Android or iOS SDK, retrieve the folder_id from the analysis’ results as shown below:

Android:

AnalysisRequest.Builder()
        ...
        .run(object : AnalysisRequest.AnalysisListener {
            override fun onSuccess(result: List<OzAnalysisResult>) {
                // save folder_id that is needed for the next step
                val folderId = result.firstOrNull()?.folderId
            }
            ...
        })

private void analyzeMedia(List<OzAbstractMedia> mediaList) {
    new AnalysisRequest.Builder()
            ...
            .run(new AnalysisRequest.AnalysisListener() {
                @Override public void onStatusChange(@NonNull AnalysisRequest.AnalysisStatus analysisStatus) {}
                @Override
                public void onSuccess(@NonNull List<OzAnalysisResult> list) {
                    String folderId = list.get(0).getFolderId();
                    }
                }
                ...
    });
}

iOS:

analysisRequest.run(
scenarioStateHandler: { state in }, 
uploadProgressHandler: { (progress) in }  
)   { (analysisResults : [OzAnalysisResult], error) in 
        // save folder_id that is needed for the next step
        let folderID = analysisResults.first?.folderID
    }
}

2. Upload your reference photo

Call POST /api/folders/{{folder_id}}/media/ method, replacing the folder_id with the ID you’ve got in the previous step. This will upload your new media to the folder where your ready-made liveness video is located.

Set the appropriate tags in the payload field of the request, depending on the nature of a reference photo that you have.

{
  "media:tags": { 
    "photo1": [
        "photo_id", "photo_id_front" // for the front side of an ID
        // OR
        "photo_selfie" // for a non-ID photo
    ]
  }
}

3. Initiate the analysis

To launch the analysis, call POST /api/folders/{{folder_id}}/analyses/ with the folder_id from the previous step. In the request body, specify the biometry check to be launched.

{
    "analyses": [
        {
            "type": "biometry"
        }
    ]
}

4. Poll for the results

Repeat calling GET /api/analyses/{{analyse_id}} with the analyse_id from the previous step once a second until the state changes from PROCESSING to something else. For a finished analysis:

• get the qualitative result from resolution (SUCCESS or DECLINED).

• get the quantitative results from analyses.results_data.min_confidence

Here is the Postman collection for this guide.

With these steps completed, you are done with adding face matching via Oz API. You will be able to access your media and analysis results in Web UI via browser or programmatically via API.

Oz API methods can be combined with great flexibility. Explore Oz API using the API Developer Guide.