The Liveness detection algorithm is intended to detect a real living person in a media.

Requirements

1. You're authorized.

2. You have already created a folder and added your media marked by correct tags into this folder. For API 4.0.8 and below, please note: the Liveness analysis works with videos and shotsets, images are ignored. If you want to analyze an image, upload it as a shotset (archive) with a single image and mark with the video_selfie_blank tag.

Processing Steps

1. Initiate the analysis for the folder: POST /api/folders/{{folder_id}}/analyses/

If you want to use a webhook for response, add it to the payload at this step, as described here.

{  
  "analyses": [
    {
      "type": "quality",
      "source_media": ["1111aaaa-11aa-11aa-11aa-111111aaaaaa"], // optional; omit to include all media from the folder
      ...
    }
  ]
}

You'll needanalysis_id or folder_id from response.

2. If you use a webhook, just wait for it to return the information needed. Otherwise, initiate polling:

• GET /api/analyses/{{analysis_id}} – for the analysis_id you have from the previous step.

• GET api/folders/{{folder_id}}/analyses/ – for all analyses performed on media in the folder with the folder_id you have from the previous step.

Repeat the check until theresolution_status and resolution fields change status to any other except PROCESSING and treat this as a result.

For the Liveness Analysis, seek the confidence_spoofing value related to the video you need. It indicates a chance that a person is not a real one.

[
  {
    // you may have multiple analyses in the list
    // pick the one you need by analyse_id or type
    "analysis_id": "1111aaaa-11aa-11aa-11aa-111111aaaaaa",
    "type": "QUALITY",
    "results_media": [
      {
        // if you have multiple media in one analysis, match score with media by source_video_id/source_shots_set_id 
        "source_video_id": "1111aaaa-11aa-11aa-11aa-111111aaaaab", // for shots_set media, the key would be source_shots_set_id 
        "results_data": 
        {
          "confidence_spoofing": 0.05790174 // quantitative score for this media
        }
      "resolution_status": "SUCCESS", // qualitative resolution (based on all media)
      ...
    ]
    ...
  }
  ...
]

The "Best shot" algorithm is intended to choose the most high-quality and well-tuned frame with a face from a video record. This algorithm works as a part of the liveness analysis, so here, we describe only the best shot part.

Processing steps

1. Initiate the analysis similar to Liveness, but make sure that extract_best_shot is set to true as shown below:

{
  "analyses": [{
    "type": "quality",
    "source_media": ["1111aaaa-11aa-11aa-11aa-111111aaaaaa"], // // optional; omit to include all media from the folder
    "params" : {
      "extract_best_shot": true // the mandatory part for the best shot analysis
    }
  }]
}

If you want to use a webhook for response, add it to the payload at this step, as described here.

2. Check and interpret results in the same way as for the pure Liveness analysis.

3. The URL to the best shot is located in the results_media -> output_images -> original_url response.