Microsoft face detection

For more information on face detection and analysis, see the Face detection concepts article. Also see the Detect API reference documentation. Modern enterprises and apps can use the the Face identification and Face verification operations to verify that a user is who they claim to be. Face identification can address "one-to-many" matching of one face in an image to a set of faces in a secure repository. Match candidates are returned based on how closely their face data matches the query face.

This scenario is used in granting building or airport access to a certain group of people or verifying the user of a device. 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 faces registered. After you create and train a group, you can do 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. The verification operation answers the question, "Do these two faces belong to the same person? Verification is also a "one-to-one" matching of a face in an image to a single face from a secure repository or photo to verify they are the same individual. Verification can be used for Identity Verification, such as a banking app that enables users to open a credit account remotely by taking a selfie and taking a picture of a photo ID to verify their identity.

For more information about identity verification, see the Facial recognition concepts guide or the Identify and Verify API reference documentation. The Find Similar operation does face matching between a target face and a set of candidate faces, finding a smaller set of faces that look similar to the target face. This is useful for doing a face search by image.

The service supports two working modes, matchPerson and matchFace. The matchPerson mode returns similar faces after filtering 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 may or may not belong to the same person.

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Privacy policy. Computer Vision can detect human faces within an image and generate rectangle coordinates for each detected face. This feature is also offered by the Azure Face service.

Use this alternative for more detailed face analysis, including face identification and head pose detection. Facial hair. The estimated facial hair presence and the length for the given face. The estimated gender of the given face. Possible values are male, female, and genderless. Whether the given face has eyeglasses. The hair type of the face. This attribute shows whether the hair is visible, whether baldness is detected, and what hair colors are detected.

Head pose. The face's orientation in 3D space. This attribute is described by the roll, yaw, and pitch angles in degrees, which are defined according to the right-hand rule.

The order of three angles is roll-yaw-pitch, and each angle's value range is from degrees to degrees. See the following diagram for angle mappings:. Whether the face has makeup. This attribute returns a Boolean value for eyeMakeup and lipMakeup.

Whether the face is wearing a mask. This attribute returns a possible mask type, and a Boolean value to indicate whether nose and mouth are covered. The visual noise detected in the face image. Whether there are objects blocking parts of the face. This attribute returns a Boolean value for eyeOccluded, foreheadOccluded, and mouthOccluded. The smile expression of the given face. This value is between zero for no smile and one for a clear smile. QualityForRecognition The overall image quality regarding whether the image being used in the detection is of sufficient quality to attempt face recognition on.

The value is an informal rating of low, medium, or high. Only "high" quality images are recommended for person enrollment, and quality at or above "medium" is recommended for identification scenarios.

The availability of each attribute depends on the detection model specified. Face attributes are predicted through the use of statistical algorithms. They might not always be accurate.