Get face detection insights
Face detection
Face detection detects faces in a media file, and then aggregates instances of similar faces into groups.
Face detection insights are generated as a categorized list in a JSON file that includes a thumbnail and either a name or an ID for each face. In the web portal, selecting a face’s thumbnail displays information like the name of the person (if they were recognized), the percentage of the video that the person appears, and the person's biography, if they're a celebrity. You can also scroll between instances in the video where the person appears.
Celebrities recognition model
The celebrities recognition model covers approximately 1 million faces and is based on commonly requested data sources. Faces that Video Indexer doesn't recognize as celebrities are detected but left unnamed. You can build your own custom person model to train Video Indexer to recognize faces that aren't recognized by default.
Important
To support Microsoft Responsible AI principles, access to face identification, customization, and celebrity recognition features is limited and based on eligibility and usage criteria. Face identification, customization, and celebrity recognition features are available to Microsoft managed customers and partners. To apply for access, use the facial recognition intake form.
Face detection use cases
The following list describes examples of common use cases for face detection:
- Summarize where an actor appears in a movie or reuse footage by deep searching specific faces in organizational archives for insight about a specific celebrity.
- Get improved efficiency when you create feature stories at a news agency or sports agency. Examples include deep searching a celebrity or a football player in organizational archives.
- Use faces that appear in a video to create promos, trailers, or highlights. Video Indexer can assist by adding keyframes, scene markers, time stamps, and labeling so that content editors invest less time reviewing numerous files.
Key terms
| Term | Definition |
|---|---|
| Face recognition | Analyzing images to identify the faces that appear in the images. This process is implemented via the Azure AI Face API. |
| Enrollment | The process of enrolling images of individuals for template creation so that they can be recognized. When a person is enrolled to a verification system that's used for authentication, their template is also associated with a primary identifier that's used to determine which template to compare against the probe template. High-quality images and images that represent natural variations in how a person looks (for instance, wearing glasses and not wearing glasses) generate high-quality enrollment templates. |
| Template | Enrolled images of people are converted to templates, which are then used for facial recognition. Machine-interpretable features are extracted from one or more images of an individual to create that individual’s template. The enrollment or probe images aren't stored by the Face API, and the original images can't be reconstructed based on a template. Template quality is a key determinant for accuracy in your results. |
View the insight JSON with the web portal
Once you have uploaded and indexed a video, insights are available in JSON format for download using the web portal.
- Select the Library tab.
- Select media you want to work with.
- Select Download and the Insights (JSON). The JSON file opens in a new browser tab.
- Look for the key pair described in the example response.
Use the API
- Use the Get Video Index request. We recommend passing
&includeSummarizedInsights=false. - Look for the key pairs described in the example response.
Important
When you review face detections in the UI, you might not see all faces that appear in the video. We expose only face groups that have a confidence of more than 0.5, and the face must appear for a minimum of 4 seconds or 10 percent of the value of video_duration. Only when these conditions are met do we show the face in the UI and in the Insights.json file. You can always retrieve all face instances from the face artifact file by using the API: https://api.videoindexer.ai/{location}/Accounts/{accountId}/Videos/{videoId}/ArtifactUrl[?Faces][&accessToken].
Example response
"faces": [
{
"id": 1785,
"name": "Emily Tran",
"confidence": 0.7855,
"description": null,
"thumbnailId": "fd2720f7-b029-4e01-af44-3baf4720c531",
"knownPersonId": "92b25b4c-944f-4063-8ad4-f73492e42e6f",
"title": null,
"imageUrl": null,
"thumbnails": [
{
"id": "4d182b8c-2adf-48a2-a352-785e9fcd1fcf",
"fileName": "FaceInstanceThumbnail_4d182b8c-2adf-48a2-a352-785e9fcd1fcf.jpg",
"instances": [
{
"adjustedStart": "0:00:00",
"adjustedEnd": "0:00:00.033",
"start": "0:00:00",
"end": "0:00:00.033"
}
]
},
{
"id": "feff177b-dabf-4f03-acaf-3e5052c8be57",
"fileName": "FaceInstanceThumbnail_feff177b-dabf-4f03-acaf-3e5052c8be57.jpg",
"instances": [
{
"adjustedStart": "0:00:05",
"adjustedEnd": "0:00:05.033",
"start": "0:00:05",
"end": "0:00:05.033"
}
]
},
]
}
]
Important
It is important to read the transparency note overview for all VI features. Each insight also has transparency notes of its own:
Face detection notes
Face detection is a tool for many industries when it's used responsibly and carefully. To respect the privacy and safety of others, and to comply with local and global regulations, we recommend that you follow these use guidelines:
- Carefully consider the accuracy of the results. To promote more accurate detection, check the quality of the video. Low-quality video might affect the insights that are presented.
- Carefully review results if you use face detection for law enforcement. People might not be detected if they're small, sitting, crouching, or obstructed by objects or other people. To ensure fair and high-quality decisions, combine face detection-based automation with human oversight.
- Don't use face detection for decisions that might have serious, adverse impacts. Decisions that are based on incorrect output can have serious, adverse impacts. It's advisable to include human review of decisions that have the potential for serious impacts on individuals.
Face detection components
The following table describes how images in a media file are processed during the face detection procedure:
| Component | Definition |
|---|---|
| Source file | The user uploads the source file for indexing. |
| Detection and aggregation | The face detector identifies the faces in each frame. The faces are then aggregated and grouped. |
| Recognition | The celebrities model processes the aggregated groups to recognize celebrities. If you've created your own people model, it also processes groups to recognize other people. If people aren't recognized, they're labeled Unknown1, Unknown2, and so on. |
| Confidence value | Where applicable for well-known faces or for faces that are identified in the customizable list, the estimated confidence level of each label is calculated as a range of 0 to 1. The confidence score represents the certainty in the accuracy of the result. For example, an 82 percent certainty is represented as an 0.82 score. |