Face recognition is identifying or confirming the identity of a person using their face. It’s something we humans do from a very early age and we take that process almost for granted. We can do this either in person, using a photo, video, or even a painting or drawing, assuming it’s accurate enough. Getting a computer to perform the same function, though, is an immensely complicated task. In order to have any true success in true facial recognition, recognizing specific faces, rather than the location or area where a face is located, requires artificial intelligence.
Will Microsoft Cognitive Services API built with Native Windows Development recognize faces? Let’s find out all the answers in this post!
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Die Kontroverse um das Erkennen von Gesichtern
As more systems have entered the market with the ability to recognize people’s faces, people began to experience some of the positive benefits, such as the ability to verify the user’s identity when unlocking a smartphone or when carrying out sensitive actions such as accessing a banking app. Before too long law enforcement agencies also began to see how they too might be able to benefit from using this technology to identify wanted criminals or to spot a potential terrorist in a crowded airport. Stopping terrorist attacks and solving crime is of course something we all want. However, like all biometrics, face recognition is not a perfect technology. The face is much more likely to change over time and can also be easily obscured, intentionally or otherwise, with things like masks, hats, and facial hair.
Warum ist Gesichtserkennung weltweit so beliebt?
Other biometric systems like fingerprints and iris scanners can also have challenges that affect their accuracy too, but less so than that of the face. Despite this, facial recognition has some benefits which might make it a good choice – for example, it is contactless, can be used from a significant distance, can be used to recognize persons of interest even if they are no longer physically present, for example via a video recording.
Facial recognition is also passive, which means you don’t need the co-operation of the people whose faces you are recognizing. The downside to that, of course, is that it can be abused by agencies or countries to track their citizens in aid to suppression of their rights, or to keep automated and possibly wide-ranging surveillance of people a government or organization might which to oppress or subjugate.
Falsch negative oder positive Aussagen können zu rechtlichen Problemen führen
Another downside of face recognition is that it can be “false negative” or “false positive”. False-negative is the situation in which the face recognition system failed to find the matching face from the database even though the database has a matching face. False-positive is the situation in which the system matches a face from a database even though the database doesn’t have any matching face. This can depend on the technology used by face recognition but even organizations with substantial technical resources can apparently Fehler beim Erkennen machen. Ultimately it’s the developer’s responsibility to choose and understand the appropriate technology for the use-case of the application they develop.
Welche Technologie steckt hinter der Gesichtserkennung?
Die Technologie hinter der Gesichtserkennung ist eine mathematische Darstellung des Gesichts. Es verwendet den Augenabstand und die Form des Kinns und vergleicht diese Vorlage dann mit vorhandenen Datensätzen in der Datenbank. Einige Systeme berechnen die Wahrscheinlichkeit des erkannten Gesichts, anstatt eine Übereinstimmung zu finden.
Wie verwende ich die Azure-Gesichts-API für die Gesichtserkennung?
Azure ist eine Cloud-Computing-Plattform. Sie sind eine großartige Alternative zu Amazon Web Services. Sie bieten Hunderte von Cloud-Computing-Diensten in vielen Kategorien. Sie haben eine Kategorie für KI und maschinelles Lernen. Es bietet die folgenden KI- und Machine-Learning-Angebote:
- Maschinelles Lernen
- Cortex-Zertifikat
- Computer Vision
- Gesicht
- Textanalyse
- Data Science Virtual Machine – Ubuntu 18.04
- InteliLiving IoT-Überwachung für ein unabhängiges Leben
- DataVisor Enterprise ML für Betrug und AML
In diesem Fall verwenden wir die Face API, die es uns ermöglicht, die Gesichtserkennung ohne maschinelles Lernen zu implementieren. Zuerst müssen Sie eine Ressourcengruppe für Ihre Anwendung erstellen. Daher wird jede von der Anwendung verwendete Ressource zusammen in Rechnung gestellt. Erstellen Sie dann eine neue Face-Ressource (in der Kategorie AI + Machine Learning). Merken Sie sich die ausgewählte Region. Gehen Sie dann zu der soeben erstellten Ressource, gehen Sie zu Schlüssel und Endpunkt und kopieren Sie einen der beiden Schlüssel.
There is a great open-source application maintained by Embercardero to show you how to use Azure Face API with your Delphi Application using REST client. It’s a Firemonkey application that can compile to Windows, Mac, iOS, and Android. You can get it from GIT hub:
https://github.com/FMXExpress/MicrosoftFacialRecognition
You just have to change the API Key and the URL you want to detect faces. Edit Params of “TRESTRequest” according to your params.
Stellen Sie mit RAD Studio Delphi und C++ Builder ganz einfach eine Verbindung zu Online-APIs und ähnlichen Ressourcen her. Sie können Laden Sie noch heute eine Testversion herunter und probieren Sie die Gesichtserkennung selbst aus.