8/22/2023 0 Comments Ml.net model builderAs a result, we will get a bounding box around the desired object on the image – a human face. So, Face Detection is the process of identifying a human face in a digital image. I am not going to talk too much about Face Detection, just because we have covered it in a previous article. We will cover how to train on the cloud soon. You can get a bigger data set and train using Azure. This is because I would like to train my model locally. This is how the training set looks like: ML.NET Model Builder: Dataset (No Mask) ML.NET Model Builder: Dataset (Mask) Given a face image, we need to classify said image in one of those two categories. And images having a person without a mask. Images that contain a face wearing a mask. Out dataset consists of two types of face images. Face Recognition with ML.NET and Model Builder application ML.NET: Data Set Either the person wears a face mask or not. Then the image is passed to the face mask detector and it generates one of two results. Once the face is detected, it is cropped. The addition to this application is the ML.NET Face Mask Recognition module. Those parameters are explained in the previous post. Because we are using the Haar Classifier we need to set up couple of parameters. Face Recognition with ML.NET and Model Builder applicationįirst, we need to open an image. The goal in this article is to detect whether a person on the image, wears a face mask. This application is continuation to the Face Detection app we built some time ago. Image Face Detection with C# NET – CODE-AI NET with Source Code (code-ai.mk) Face Detection C# Application If you want to learn more continue reading up on the following link. So, I will only focus on some key points. This article is not about ML.NET or the Model Builder as a tool. How to use ML.NET Model Builder for Image Classification To make sure that you have your environment set correctly you can go back and check my article on how to activate and use ML.NET with Model Builder. If you use an older version of Visual Studio, then the Model Builder might be in a Preview Mode. Keep in mind is that this tool is shipped with Visual Studio 2019. Because of this, the Model Builder can be used by anyone trying to solve a Machine Learning problem without a prior knowledge. As a result, the Machine Learning complexity is delegated to AutoML. AutoML allows us to load the dataset and train the model right away. Because ML.NET Model Builder supports AutoML, it is very easy to find the machine learning model best suited for our scenario. This tool is especially useful for developers that have no prior knowledge on Machine Learning. Model Builder is a simple UI tool for developers to build and train custom machine learning models in their. In this tutorial we will use the ML.NET Model Builder to create a face mask detection module. That is because we are not using images of faces that are linked to their identities. So, it can not link a face to a specific person. As you can see our application is not identifying faces in any way. Then the cropped face will be handed over to the face mask recognition system. We will use a famous algorithm to extract faces from an image. And that one is responsible for Face Detection. And the other set will consist of pictures where the person does not wear a mask.īut before we do that, we must have another module in place. One set to teach the algorithm how to recognize a person with a face mask on. Like others before us, we will train a ML.NET Model on two sets of pictures. And we will see the correctness of this statement in just a bit. Is it breaking our privacy rights? Well mask recognition software in theory bypasses the privacy issues because programs do not actually identify people. What I am trying to convey is that a lot of companies had to solve this problem and adapt their system accordingly.Ī lot of people are rightfully worried about this type of software. Otherwise, the normal face recognition workflow is executed. If the person is wearing a face mask, they are prompted to enter their password. Instead of recognizing the person on the image, they now scan for a person wearing a mask. Because of that a lot of companies issued an update to their facial recognition system. Like we mentioned before, wearing a mask obstructs the recognition process. Face Mask Detection using ML.NET ConclusionĪ Facial Recognition software analyzes the features around the eye, nose, mouth, and ears to identify the person whose picture is supplied.
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