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This is a reference to the deprecated offline documentation. It needs to be replaced with new instructions how to obtain SDK version. |
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This FAQ entry contains pointers to the visage|SDK offline documentation which is deprecated. These pointers need to be replaced with links to online documentation. |
Can I use visage|SDK with C#?
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Can I use visage|SDK with Java?
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Can I use visage|SDK in WebView (in iOS and Android)?
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Important notes:
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The package you have received is visage|SDK 8.4. The latest release is visage|SDK 8.7 but that is not available for rPI yet. If your initial tests prove interesting we will need to discuss the possibility to build the latest version on-demand for you. visage|SDK 8.7 provides better performance and accuracy, but the API is mostly unchanged so you can run relevant initial tests.
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visage|SDK 8.4 for rPI has been tested with rPI3b+; it should work with rPI4 but we have not tested that.
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On iOS, the HTML5 demos work in Safari browser version 11 and higher. They do not work in Chrome and Firefox browsers due to limitations on camera access.
(https://stackoverflow.com/questions/59190472/webrtc-in-chrome-ios)
Which browsers does visage|SDK for HTML5 support?
The HTML5 demo page contains the list of supported browsers: https://visagetechnologies.com/demo/#supported-browsers
Please note that the current HTML5 demos have not been optimized for use in mobile browsers so for best results it is recommended to use a desktop browser.
Internet connection and privacy issues
Does my device/app need internet connection?
Applications developed using visage|SDK do not need an internet connection for operation.
Internet connection is needed only for license registration, which happens only once, when the application is used for the first time.
For hardware devices, this can typically be done at installation time – you would connect the device to the network, run the application once, the license registration happens automatically, and after that no network connection is needed.
How do you handle privacy of user data? Does visage|SDK store or send it to a cloud server?
visage|SDK never transfers any user information to any server.
visage|SDK never automatically stores any user information locally,
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Face Recognition
How far from a camera can a face be recognized?
This depends on camera resolution. Face Recognition works best when the size of the face in the image is 150 pixels or more.
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See also: |
High-level functionalities
How do I perform liveness detection with visage|SDK?
visage|SDK includes active liveness detection: the user is required to perform a simple facial gesture (smile, blink or eyebrow raising) and face tracking is then used to verify that the gesture is actually performed. You can configure which gesture(s) you want to include. As app developer you also need to take care of displaying appropriate messages to the user.
All visage|SDK packages include the API for liveness detection. However, only the visage|SDK for Windows and visage|SDK for Android contain a ready-to-run demo of Liveness Detection. So, for a quick test of the liveness detection function it would probably be the easiest to downoad visage|SDK for Windows, run “DEMO_FaceTracker2.exe” and select “Perform Liveness” from the Liveness menu.
The technical demos in Android and Windows packages of visage|SDK include the source code intended to help you integrate liveness detection into your own application.
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How do I perform identification of a person from a database?
This article outlines the implementation of using face recognition for identifying a person from a database of known people. It may be applied to cases such as whitelists for access control or attendance management, blacklists for alerts and similar. The main processes involved in implementing this scenario are registration and matching, as follows.
Registration
Assuming that you have an image and an ID (name, number or similar) for each person, you register each person by storing their face descriptor into a gallery (database). For each person, the process is as follows:
Locate the face in the image:
To locate the face, you can use detection (for a single image) or tracking (for a series of images from a camera feed).
See function VisageSDK::VisageTracker::track() or VisageFeaturesDetector::detectFacialFeatures().
Each of these functions returns the number of faces in the image - if there is not exactly one face you may report an error or take other action.
Furthermore, these functions return the FaceData structure for each detected face, containing the face location.
Use VisageFaceRecognition.AddDescriptor() to get the face descriptor and add it to the gallery of known faces together with the name or ID of the person.
The descriptor is an array of short integers that describes the face - similar faces will have similar descriptors.
The gallery is simply a database of face descriptors, each with attached ID.
Note that you could store the descriptors in your own database, without using the provided gallery implementation.
Save the gallery using VisageFaceRecognition.SaveGallery().
Matching
In this stage, you match a new facial image (for example, a person arriving at a gate, reception, control point or similar) against the previously stored gallery, and obtain IDs of one or more most similar persons registered in the gallery.
First, locate the face(s) in the new image.
The steps are the same as explained above in the Registration part. You obtain a FaceData structure for each located face.
Pass the FaceData to VisageFaceRecognition.ExtractDescriptor() to get the face descriptor of the person.
Pass this descriptor to VisageFaceRecognition.Recognize(), which will match it to all the descriptors you have previously stored in the gallery and return the name/ID of the most similar person (or a desired number of most similar persons);
the Recognize() function also returns a similarity value, which you may use to cut off the false positives.
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How do I perform verification of a live face vs. ID photo?
The scenario is verification of a live face image against the image of a face from an ID. It is done in four main steps:
Locate the face in each of the two images;
Extract the face descriptor from each of the two faces;
Compare the two descriptors to obtain the similarity value;
Compare the similarity value to a chosen threshold, resulting in a match or non-match.
These steps are described here with further detail:
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Note: the ID image should be cropped so that the ID is occupying most of the image (if the face on the ID is too small relative to the whole image it might not be detected).
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How do I determine if a person is looking at the screen?
First, let me say that visage|SDK does not have an out-of-the box option to determine if the person is looking at the screen. However, it should not be too difficult to implement that. What visage|SDK does provide is:
The 3D position of the head with respect to the camera;
The 3D gaze direction vector.
Now, if you also know the size of the screen and the position of the camera with respect to the screen, you can:
Calculate the 3D position of the head with respect to your screen;
Cast a ray (line) from the 3D head position in the direction of the gaze;
Verify if this line intersects the screen.
Please also note that the estimated gaze direction may be a bit unstable (the gaze vectors appearing “shaky”) due to the difficulty of accurately locating the pupils. At the same time, the 3D head pose (head direction) is much more stable. Because people usually turn their head in the direction where they are looking, it may also be interesting to use the head pose as the approximation of the direction of gaze.
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Troubleshooting
I am using visage|SDK FaceRecognition gallery with a large number of descriptors (100.000+) and it takes 2 minutes to load the gallery. What can I do about it?
visage|SDK FaceRecognition simple gallery implementation was not designed for a use case with a large number of descriptors.
Use API functions that provide raw descriptor output (extractDescritor) and descriptor similarity comparison function (descriptorSimilarity) to implement your own gallery solution in technology of your choice is appropriate for your use case.
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I want to change the camera resolution in Unity application VisageTrackerDemo. Is this supported and how can I do this?
Depending on the platform it’s already possible, out of the box.
On Windows and Android, the camera resolution can be changed via Tracker object properties defaultCameraWidth and defaultCameraHeight within the Unity Editor. When the default value -1 is used, the resolution is set to 800 x 600 in the native VisageTrackerUnityPlugin.
On iOS it’s not possible to change the resolution out of the box from the demo application. The camera resolution is hard-coded to 480 x 600 within the native VisageTrackerUnityPlugin.
VisageTrackerUnityPlugin is provided with full source code within the package distribution.
I am getting EntryPointNotFoundException when attempting to run a Unity application in the Editor. Why does my Unity application does not work?
Make sure that you have followed all the steps from the documentation Building and running Unity application.
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Verify that the build target and visage|SDK platform match. For example, running visage|SDK for Windows inside the Unity Editor and for a Standalone application will work since both are run on the same platform. Attempting to run visage|SDK for iOS inside the Unity Editor on a MacOS will output an error because iOS architecture does not match MacOS architecture.
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From the previous step you have one FaceData structure for the ID image and one FaceData structure for the live image.
Pass each image with its corresponding FaceData to the function VisageFaceRecognition::extractDescriptor().
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Pass the two descriptors to the function VisageFaceRecognition::descriptorsSimilarity() to compare the two descriptors to each other and obtain the measure of their similarity. This is a float value between 0 (no similarity) and 1 (perfect similarity).
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If the similarity is greater than a chosen threshold, consider that the live face matches the ID face.
By choosing the threshold, you control the trade-off between False Positives and False Negatives:
If the threshold is very high, there will be virtually no False Positives, i.e. the system will never declare a correct match when in reality the live person is not the person in the ID.
However, with a very high threshold a False Negative may happen more often - not matching a person who really is the same as in the ID, resulting in an alert that will need to be handled in an appropriate way (probably requiring human intervention).
Conversely, with a very low threshold, such “false alert” will virtually never be raised, but the system may then fail to detect True Negatives - the cases when the live person really does not match the ID.
There is no “correct” threshold, because it depends on the priority of a specific application. If the priority is to avoid false alerts, threshold may be lower; if the priority is to avoid undetected non-matches then the threshold should be higher.
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The error you received indicates that the license keys were somehow modified. Please try using a clean copy of the license key that was sent to you to see if the error still occurs.
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-1 - License Key sent to VTLS server for validation was malformed
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-2 - Device ID sent to VTLS server for validation was malformed
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-3 - License does not exist on VTLS server
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-4 - License is blocked on VTLS server
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-5 - License expired
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-6 - Installations limit reached
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