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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A polêmica de reconhecer rostos
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.
Por que o reconhecimento facial é tão popular em todo o mundo?
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.
Falsos negativos ou positivos podem levar a problemas jurídicos
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 cometer erros no reconhecimento. Ultimately it’s the developer’s responsibility to choose and understand the appropriate technology for the use-case of the application they develop.
Qual é a tecnologia por trás do reconhecimento facial?
A tecnologia por trás do reconhecimento facial está obtendo uma representação matemática do rosto. Ele usa a distância entre os olhos e o formato do queixo e, em seguida, compara esse modelo com os registros existentes no banco de dados. Alguns sistemas calculam a probabilidade do rosto detectado em vez de encontrar uma correspondência.
Como usar a API facial do Azure para reconhecimento facial?
Azure é uma plataforma de computação em nuvem. Eles são uma ótima alternativa para Amazon Web Services. Eles fornecem centenas de serviços de computação em nuvem em muitas categorias. Eles têm uma categoria para IA e aprendizado de máquina. Ele tem as seguintes ofertas de IA e aprendizado de máquina:
- Aprendizado de Máquina
- Cortex Certifai
- Visão Computacional
- Cara
- Análise de Texto
- Data Science Virtual Machine – Ubuntu 18.04
- InteliLiving IoT Monitoring for Independent Living
- DataVisor Enterprise ML para Fraude e AML
Nesse caso, usamos a API Face, que nos permite implementar o reconhecimento facial sem qualquer experiência de aprendizado de máquina. Primeiro, você deve criar um grupo de recursos para seu aplicativo. Portanto, todos os recursos usados pelo aplicativo serão cobrados juntos. Em seguida, crie um novo recurso Face (na categoria AI + Machine learning). Lembre-se da região que você selecionou. Em seguida, vá para o recurso que você acabou de criar e vá para Chaves e Endpoint e copie uma das duas chaves.
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.
Conecte-se facilmente a APIs online e recursos semelhantes usando RAD Studio Delphi e C ++ Builder. Você pode baixe uma cópia de teste hoje e tente reconhecer o rosto por si mesmo.