Sample Video For Face Detection Download
Sasha Stolt
Note: This sample is part of a large collection of UWP feature samples.You can download this sample as a standalone ZIP filefrom docs.microsoft.com,or you can download the entire collection as a singleZIP file, but besure to unzip everything to access shared dependencies. For more info on working with the ZIP file,the samples collection, and GitHub, see Get the UWP samples from GitHub.For more samples, see the Samples portal on the Windows Dev Center.
The FaceDetector is intended to operate on a static image or a single frame of video and is not optimized for video playback or live camera streams. In order to track human faces in real-time, either through a live stream or a video clip, use the FaceTracker API instead.
Here are examples of some popular use cases that you can accomplish using transformations based on detected faces (combined with other transformations). Click each image to see the URL parameters applied in each case:
Cloudinary supports built-in face-detection capabilities that allow you to intelligently crop your images. To automatically crop an image so that the detected face(s) is used as the center of the derived picture, set the gravity parameter to one of the following values:
To create a 200x100 version with the fill cropping mode to keep as much as possible of the original image, and using the default center gravity without face detection (for comparison):
You can also automatically crop exactly to the region determined by the face-detection mechanism without defining resize dimensions for the original image. The following example uses the crop mode together with face gravity for cropping the original image to the face of the woman:
For example, adding an overlay of the purple-mask image over both of the faces detected in the young-couple image, where each mask is resized to the same width as the detected face with the region_relative flag:
You can use the getinfo flag together with a face-detection gravity option to return the coordinates of facial landmarks via a completely client-side operation. Using this information, you can, for example, calculate the x and y offsets to specify the exact position of an overlay on a face, or you could pass the data back to other functions in your application.
For example, you can get the facial landmark coordinates in this image by using the getinfo flag together with a crop and a gravity option that detects a face. In this example we have used g_face, but we could have also used g_auto:face, or even just g_auto (as the g_auto default behavior is to apply g_auto:faces).
You may want to detect a face to avoid positioning an overlay on it. In this case, you can use g_auto:face_avoid together with the getinfo flag to find the area of the image that is least likely to include a face. Then, use these coordinates when adding the overlay.
With the Advanced Facial Attributes Detection add-on, you can extend the Cloudinary built-in features that involve semantic photo data extraction, image cropping and the positioning of image overlays. When using the add-on, your images are further processed and additional advanced face attributes are automatically extracted. Cloudinary can then use these additional details to smartly crop, position, rotate and overlay images according to the position of the detected faces or eyes.
When using either the crop or thumb cropping modes and setting the gravity parameter to one of the face-detection values, the resulting image is delivered at a default zoom level. To control how much of the original image surrounding the face to keep, use the zoom parameter (z for URLs). This parameter accepts a decimal value that sets the new zoom level as a multiplier of the default zoom setting: a value less than 1.0 zooms out and a value greater than 1.0 zooms in. For example, z_0.5 halves the default zoom to 50% and zooms out to include more of the background around the face, while z_2.0 doubles the default zoom to 200% and zooms in to include less of the background around the face.
For example, the Cloudinary Solutions team built an open-source library that can learn and then auto-tag faces based on a privately maintained mapping between faces and names. This functionality could be used for internal applications, such as auto-mapping employee head shots to employee profile pages or tagging students in school event photos on a university website.
This open-source library uses an Amazon Rekognition lambda function, which is triggered by the notification webhook that's sent when photos are uploaded to a specified folder in Cloudinary, and afterwards uses Cloudinary's Amazon Rekognition Auto-Tagging add-on to automatically tag photos if they contain faces learned from that list.
Install npm andnode-canvas. The sample code includes apackage.json to install all dependencies usingthe command: npm install. Note that node-canvas has additionaldependencies you may need to install - see the node-canvas installationdoc for more information.
Congratulations - you've detected the faces in your image! Theresponse to our face annotation request includes a bunch ofmetadata about the detected faces, which include coordinatesof a polygon encompassing the face. At this point, though, thisis only a list of numbers. Let's use them to confirm that you have, in fact,found the faces in your image. We'll draw polygons onto a copy of the image,using the coordinates returned by the Vision API:
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If you want to take it one step further and recognize individual faces - perhaps to detect and recognize your face amongst many strangers - the task is surprisingly difficult. This is mainly due to the large amount of image pre-processing involved. But if you are willing to tackle the challenge, it is possible by using machine learning algorithms as described here.
We unlock our iPhones with a glance and wonder how Facebook knew to tag us in that photo. But face recognition, the technology behind these features, is more than just a gimmick. It is employed for law enforcement surveillance, airport passenger screening, and employment and housing decisions. Despite widespread adoption, face recognition was recently banned for use by police and local agencies in several cities, including Boston and San Francisco. Why? Of the dominant biometrics in use (fingerprint, iris, palm, voice, and face), face recognition is the least accurate and is rife with privacy concerns.
Several avenues are being pursued to address these inequities. Some target technical algorithmic performance. First, algorithms can train on diverse and representative datasets, as standard training databases are predominantly White and male. Inclusion within these datasets should require consent by each individual. Second, the data sources (photos) can be made more equitable. Default camera settings are often not optimized to capture darker skin tones, resulting in lower-quality database images of Black Americans. Establishing standards of image quality to run face recognition, and settings for photographing Black subjects, can reduce this effect. Third, to assess performance, regular and ethical auditing, especially considering intersecting identities (i.e. young, darker-skinned, and female, for example), by NIST or other independent sources can hold face recognition companies accountable for remaining methodological biases.
Other approaches target the application setting. Legislation can monitor the use of face recognition technology, as even if face recognition algorithms are made perfectly accurate, their contributions to mass surveillance and selective deployment against racial minorities must be curtailed. Multiple advocacy groups have engaged with lawmakers, educating on racial literacy in face recognition and demanding accountability and transparency from producers. For example, the Safe Face Pledge calls on organizations to address bias in their technologies and evaluate their application. Such efforts have already achieved some progress. The 2019 Algorithmic Accountability Act empowered the Federal Trade Commission to regulate companies, enacting obligations to assess algorithmic training, accuracy, and data privacy. Furthermore, several Congressional hearings have specifically considered anti-Black discrimination in face recognition. The powerful protests following the murder of George Floyd also drove significant change. Congressional Democrats introduced a police reform bill containing stipulations to restrain the use of face recognition technologies. More astonishing was the tech response: IBM discontinued its system, Amazon announced a one-year freeze on police use of Rekognition, and Microsoft halted sales of its face recognition technology to the police until federal regulations are instituted. These advances have supported calls for more progressive legislation, such as the movements to reform or abolish policing. For now, the movement for equitable face recognition is intertwined with the movement for an equitable criminal justice system.
Object detection and tracking are important in many computer vision applications including activity recognition, automotive safety, and surveillance. In this example, you will develop a simple face tracking system by dividing the tracking problem into three parts:
First, you must detect the face. Use the vision.CascadeObjectDetector object to detect the location of a face in a video frame. The cascade object detector uses the Viola-Jones detection algorithm and a trained classification model for detection. By default, the detector is configured to detect faces, but it can be used to detect other types of objects.
To track the face over time, this example uses the Kanade-Lucas-Tomasi (KLT) algorithm. While it is possible to use the cascade object detector on every frame, it is computationally expensive. It may also fail to detect the face, when the subject turns or tilts his head. This limitation comes from the type of trained classification model used for detection. The example detects the face only once, and then the KLT algorithm tracks the face across the video frames.
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