Detect Faces With Azure AI Vision Service

By Ashwin Venugopal -

 





Understand Capabilities Of The Face Service

The Face service provides comprehensive facial detection, analysis, and recognition capabilities. The Face service provides functionality that you can use for:

  • Face detection - for each detected face, the results include an ID that identifies the face and the bounding box coordinates indicating its location in the image.

  • Face attribute analysis - you can return a wide range of facial attributes, including-

  1. Head pose (pitch, roll, and yaw orientation in 3D space)
  2. Glasses (NoGlasses, ReadingGlasses, Sunglasses, or Swimming Goggles)
  3. Blur (low, medium, or high)
  4. Exposure (underExposure, goodExposure, or overExposure)
  5. Noise (visual noise in the image)
  6. Occlusion (objects obscuring the face)
  7. Accessories (glasses, headwear, mask)
  8. QualityForRecognition (low, medium, or high)

  • Facial landmark location - coordinates for key landmarks in relation to facial features (for example, eye corners, pupils, tip of nose, and so on).

  • Face comparison - you can compare faces across multiple images for similarity (to find individuals with similar facial features) and verification (to determine that a face in one image is the same person as a face in another image).

  • Facial recognition - you can train a model with a collection of faces belonging to specific individuals, and use the model to identify those people in new images.

  • Facial liveness - liveness can be used to determine if the input video is a real stream or a fake to prevent bad intentioned individuals from spoofing the recognition system.

You can provision Face as a single-service resource, or you can use the Face API in a multi-service Azure AI Services resource. If you want to use the identification, recognition, and verification features of Face, you will need to apply for the Limited Access policy and get approval before these features are available.

Compare and Match Detected Faces

A distinct ID is given to each face that the Face service detects, and this ID is kept in the service resource for a whole day. Other than the person's face traits, the ID is a GUID that doesn't reveal anything about who they are.

While the identified face ID is cached, later photos can be used to confirm that the same person appears in two images or to compare the new faces to the cached identity to see if they are comparable that is, if they share similar facial traits.

In situations where it's crucial to verify that the same individual is present twice without needing to know their true identity, the ability to compare faces anonymously can be helpful. For instance, by photographing individuals as they enter and exit a restricted area to confirm that everyone has left.

Implement Facial Recognition

For scenarios where you need to positively identify individuals, you can train a facial recognition model using face images. To train a facial recognition model with the Face service:

  • Create a Person Group that defines the set of individuals you want to identify (for example, employees).

  • Add a Person to the Person Group for each individual you want to identify.

  • Add detected faces from multiple images to each person, preferably in various poses. The IDs of these faces will no longer expire after 24 hours (so they're now referred to as persisted faces).

  • Train the model.

The trained model is stored in your Face (or Azure AI Services) resource, and can be used by client applications to:

  1. Identify individuals in images.
  2. Verify the identity of a detected face.
  3. Analyze new images to find faces that are similar to a known, persisted face.

Conclusion

We have successfully learnt about face detection, analysis, and identification. 
















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