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Sep 12, 2016  Detector Settings. The Detector component receives images and runs face detection/tracking on the series of images that it receives. The following default properties were used in creating the Detector above: mode = fast: This indicates that the face detector can use optimizations that favor speed over accuracy.

[Some information relates to pre-released product which may be substantially modified before it's commercially released. Microsoft makes no warranties, express or implied, with respect to the information provided here.]

This topic shows how to use the FaceDetector to detect faces in an image. The FaceTracker is optimized for tracking faces over time in a sequence of video frames.

For an alternative method of tracking faces using the FaceDetectionEffect, see Scene analysis for media capture.

The code in this article was adapted from the Basic Face Detection and Basic Face Tracking samples. You can download these samples to see the code used in context or to use the sample as a starting point for your own app.

Detect faces in a single image

Amc theaters near me. The FaceDetector class allows you to detect one or more faces in a still image.

This example uses APIs from the following namespaces.

Declare a class member variable for the FaceDetector object and for the list of DetectedFace objects that will be detected in the image.

Face detection operates on a SoftwareBitmap object which can be created in a variety of ways. In this example a FileOpenPicker is used to allow the user to pick an image file in which faces will be detected. For more information about working with software bitmaps, see Imaging.

Use the BitmapDecoder class to decode the image file into a SoftwareBitmap. The face detection process is quicker with a smaller image and so you may want to scale the source image down to a smaller size. This can be performed during decoding by creating a BitmapTransform object, setting the ScaledWidth and ScaledHeight properties and passing it into the call to GetSoftwareBitmapAsync, which returns the decoded and scaled SoftwareBitmap.

In the current version, the FaceDetector class only supports images in Gray8 or Nv12. The SoftwareBitmap class provides the Convert method, which converts a bitmap from one format to another. This example converts the source image into the Gray8 pixel format if it is not already in that format. If you want, you can use the GetSupportedBitmapPixelFormats and IsBitmapPixelFormatSupported methods to determine at runtime if a pixel format is supported, in case the set of supported formats is expanded in future versions.

Instantiate the FaceDetector object by calling CreateAsync and then calling DetectFacesAsync, passing in the bitmap that has been scaled to a reasonable size and converted to a supported pixel format. This method returns a list of DetectedFace objects. ShowDetectedFaces is a helper method, shown below, that draws squares around the faces in the image.

Be sure to dispose of the objects that were created during the face detection process.

To display the image and draw boxes around the detected faces, add a Canvas element to your XAML page.

Define some member variables to style the squares that will be drawn.

In the ShowDetectedFaces helper method, a new ImageBrush is created and the source is set to a SoftwareBitmapSource created from the SoftwareBitmap representing the source image. The background of the XAML Canvas control is set to the image brush.

If the list of faces passed into the helper method isn't empty, loop through each face in the list and use the FaceBox property of the DetectedFace class to determine the position and size of the rectangle within the image that contains the face. Because the Canvas control is very likely to be a different size than the source image, you should multiply both the X and Y coordinates and the width and height of the FaceBox by a scaling value that is the ratio of the source image size to the actual size of the Canvas control.

Track faces in a sequence of frames

If you want to detect faces in video, it is more efficient to use the FaceTracker class rather than the FaceDetector class, although the implementation steps are very similar. The FaceTracker uses information about previously processed frames to optimize the detection process.

Declare a class variable for the FaceTracker object. This example uses a ThreadPoolTimer to initiate face tracking on a defined interval. A SemaphoreSlim is used to make sure that only one face tracking operation is running at a time.

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To initialize the face tracking operation, create a new FaceTracker object by calling CreateAsync. Initialize the desired timer interval and then create the timer. The ProcessCurrentVideoFrame helper method will be called every time the specified interval elapses.

The ProcessCurrentVideoFrame helper is called asynchronously by the timer, so the method first calls the semaphore's Wait method to see if a tracking operation is ongoing, and if it is the method returns without trying to detect faces. At the end of this method, the semaphore's Release method is called, which allows the subsequent call to ProcessCurrentVideoFrame to continue.

The FaceTracker class operates on VideoFrame objects. There are multiple ways you can obtain a VideoFrame including capturing a preview frame from a running MediaCapture object or by implementing the ProcessFrame method of the IBasicVideoEffect. This example uses an undefined helper method that returns a video frame, GetLatestFrame, as a placeholder for this operation. For information about getting video frames from the preview stream of a running media capture device, see Get a preview frame.

As with FaceDetector, the FaceTracker supports a limited set of pixel formats. This example abandons face detection if the supplied frame is not in the Nv12 format.

Call ProcessNextFrameAsync to retrieve a list of DetectedFace objects representing the faces in the frame. After you have the list of faces, you can display them in the same manner described above for face detection. Note that, because the face tracking helper method is not called on the UI thread, you must make any UI updates in within a call CoreDispatcher.RunAsync.

Related topics

Biometric identity verification for large-scale high-security applications

The Face Verification SDK is designed for integration of facial authentication into enterprise and consumer applications for mobile devices and PCs. The simple API of the library component helps to implement solutions like payment, e-services and all other apps that need enhanced security through biometric face recognition, while keeping their overall size small for easy deployment to millions of users.

Different liveness detection functionalities are included to implement anti-spoofing mechanism with the possibility of configuring the balance between security and usability of the application.

Available on Android, iOS, Microsoft Windows, macOS and Linux platforms.

Download Brochure (PDF).

Download demo app for Android.

Download SDK Trial.

• Compact library for deployment on mobile devices.
• Based on VeriLook technology with millions of deployments worldwide.
• Simple, high-level API.
• Privacy and security.
• Live face detection prevents spoofing.
• Android, iOS, Microsoft Windows, macOS and Linux supported.
• Programming samples in multiple languages included.
• Reasonable prices, flexible licensing and free customer support.

The Face Verification SDK is intended for developing applications which perform end-user identity verification in mass scale systems like:

• online banking and e-shops;
• government e-services;
• social networks and media sharing services.

The Face Verification SDK is based on the VeriLook algorithm, which provides advanced face localization, enrollment and matching using robust digital image processing algorithms based on deep neural networks. The SDK offers these features for large-scale identity verification systems:

• Simple, high-level API. The API provides operations for creating face templates from camera or still image, face verification against a specific previously created face template, importing face templates which were created with VeriLook algorithm, as well as performing face liveness check.
• Privacy and security. The face images and biometric templates are kept on the client-side and do not leave the end-user device. The face images are required only for template creation and face liveness detection, thus they can be disposed just after performing these operations.
• Live face detection. A conventional face identification system can be tricked by placing a photo in front of the camera. Face Verification SDK is able to prevent this kind of security breach by determining whether a face in a video stream is 'live' or a photograph. The liveness detection can be performed in passive mode, when the engine evaluates certain facial features, and in active mode, when the engine evaluates user's response to perform actions like blinking or head movements.
• Face image quality determination. A quality threshold can be used during face enrollment to ensure that only the best quality face template will be stored into database.
• Tolerance to face position. The Face Verification SDK allows head roll, pitch and yaw variation up to 15 degrees in each direction from the frontal position during face detection and up to 45 degrees in each direction during face tracking.