Microsoft Face API: Windows Client Library and sample is a WPF app that demonstrates several scenarios of Face detection, analysis, and identification. FamilyNotes UWP app is a Universal Windows Platform (UWP) app that uses face identification along with speech, Cortana, ink, and camera in a family note-sharing scenario. Feb 20, 2019 Microsoft Face API: Windows Client Library and sample is a WPF app that demonstrates several scenarios of Face detection, analysis, and identification. FamilyNotes UWP app is a Universal Windows Platform (UWP) app that uses face identification along with speech, Cortana, ink, and camera in a family note-sharing scenario. All the services you can connect to using Microsoft Flow. Save time by automating everyday tasks.
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Azure's Computer Vision service provides developers with access to advanced algorithms that process images and return information. To analyze an image, you can either upload an image or specify an image URL. The images processing algorithms can analyze content in several different ways, depending on the visual features you're interested in. For example, Computer Vision can determine if an image contains adult or racy content or find all of the human faces in an image.
You can use Computer Vision in your application by using either a native SDK or invoking the REST API directly. This page broadly covers what you can do with Computer Vision.
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Analyze images for insight![]()
You can analyze images to detect and provide insights about their visual features and characteristics. All of the features in the table below are provided by the Analyze Image API.
Extract text from images
You can use Computer Vision Read API to extract printed and handwritten text from images into a machine-readable character stream. The Read API uses our latest models and works with text on a variety of surfaces and backgrounds, such as receipts, posters, business cards, letters, and whiteboards. Currently, English is the only supported language.
You can also use the optical character recognition (OCR) API to extract printed text in several languages. If needed, OCR corrects the rotation of the recognized text and provides the frame coordinates of each word. OCR supports 25 languages and automatically detects the language of the recognized text.
Moderate content in images
You can use Computer Vision to detect adult and racy content in an image and return a confidence score for both. You can set the filter for adult and racy content detection on a sliding scale to accommodate your preferences.
Use containers
Use Computer Vision containers to recognize printed and handwritten text locally by installing a standardized Docker container closer to your data.
Image requirements
Computer Vision can analyze images that meet the following requirements:
Data privacy and security
As with all of the Cognitive Services, developers using the Computer Vision service should be aware of Microsoft's policies on customer data. See the Cognitive Services page on the Microsoft Trust Center to learn more.
Next steps
Get started with Computer Vision by following a quickstart guide:
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The Azure Cognitive Services Face API provides algorithms that are used to detect, recognize, and analyze human faces in images. The ability to process human face information is important in many different software scenarios. Example scenarios are security, natural user interface, image content analysis and management, mobile apps, and robotics.
The Face API provides several different functions. Each function is outlined in the following sections. Read on to learn more about them.
Face detection
The Face API detects human faces in an image and returns the rectangle coordinates of their locations. Optionally, face detection can extract a series of face-related attributes. Examples are head pose, gender, age, emotion, facial hair, and glasses.
Note
The face detection feature is also available through the Computer Vision API. If you want to do further operations with face data, use the Face API, which is the service discussed in this article.
For more information on face detection, see the Face detection concepts article. Also see the Detect API reference documentation.
Face verification
The Verify API performs an authentication against two detected faces or from one detected face to one person object. Practically, it evaluates whether two faces belong to the same person. This capability is potentially useful in security scenarios. For more information, see the Face recognition concepts guide or the Verify API reference documentation.
Find similar faces
The Find Similar API compares a target face with a set of candidate faces to find a smaller set of faces that look similar to the target face. Two working modes, matchPerson and matchFace, are supported. The matchPerson mode returns similar faces after it filters for the same person by using the Verify API. The matchFace mode ignores the same-person filter. It returns a list of similar candidate faces that might or might not belong to the same person.
The following example shows the target face:
And these are the candidate faces:
To find four similar faces, the matchPerson mode returns a and b, which show the same person as the target face. The matchFace mode returns a, b, c, and d, exactly four candidates, even if some aren't the same person as the target or have low similarity. For more information, see the Face recognition concepts guide or the Find Similar API reference documentation.
Face grouping
The Group API divides a set of unknown faces into several groups based on similarity. Each group is a disjoint proper subset of the original set of faces. All of the faces in a group are likely to belong to the same person. There can be several different groups for a single person. The groups are differentiated by another factor, such as expression, for example. For more information, see the Face recognition concepts guide or the Group API reference documentation.
Person identification
The Identify API is used to identify a detected face against a database of people. This feature might be useful for automatic image tagging in photo management software. You create the database in advance, and you can edit it over time.
The following image shows an example of a database named
'myfriends' . Each group can contain up to 1 million different person objects. Each person object can have up to 248 faces registered.
After you create and train a database, you can perform identification against the group with a new detected face. If the face is identified as a person in the group, the person object is returned. 64 bit system download.
For more information about person identification, see the Face recognition concepts guide or the Identify API reference documentation.
Use containers
Use the Face container to detect, recognize, and identify faces by installing a standardized Docker container closer to your data.
Sample apps
The following sample applications show a few ways to use the Face API:
Web Api
Data privacy and security
As with all of the Cognitive Services resources, developers who use the Face service must be aware of Microsoft's policies on customer data. For more information, see the Cognitive Services page on the Microsoft Trust Center.
Next stepsMicrosoft Team Services Api
Follow a quickstart to implement a face-detection scenario in code:
Microsoft Access Api
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