- Install intel oneAPI Toolkit by the following link https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html?operatingsystem=window&distributions=offline
- select the OS which you are using in your PC.
- select to use file in online or offline.(we used offline package)
- Download The zip file from the github and extract it
- Using any software for example VS Code and Pycharm etc
- Open folder FaceDetectionLive.py
- Run it
- Then it will open webcam to live view of the poeple which the human face is detected and Higlights the detected face with a Green box
Face recognition is a type of biometric software that identifies individuals by their facial features. This software is developed to identify faces individually or in a group. They recognise the facial features of individuals and detect human faces accurately. It also counts the number of faces within the scope of the camera view. This software can be integrated into a surveillance camera and can be used to detect people stranded during calamities.
What is Face Recognition Machine Learning Model? Face recognition is the process of identifying a face from a digital image or a video frame from a video source. The task of face recognition involves comparing selected facial features from the image with those of other images to find the closest match. Face Recognition Machine Learning (FRML) model is used for this purpose and it has been implemented using TensorFlow, Keras, and OpenCV libraries. This model allows us to recognize faces by extracting key points from them, then we can compare these key points with other images that contain faces to find similarities between them. History of Face Recognition Machine Learning Model : Face recognition is a technology that has been around for decades. It was first developed in the 1960s and 1970s, but it wasn't until recently that we've seen significant advancements in this area of machine learning. In this section, we'll take a look at the history of face recognition machine learning models and key milestones along the way. Technologies Used: We have used the Numpy,scikit,sk,scikit- learn module in the intel analytics cloud. Tool kits used: intel oneAPI base toolkit. Under that, we have used intel distribution python. (IDP)
Security - Face recognition can be used to identify people who should not be allowed access to certain areas or buildings, such as airports and government buildings. It can also be used to prevent identity theft by identifying those who have stolen someone else's identity. Surveillance - Face recognition is commonly used by police departments across the country to monitor crowds at large events such as protests and sporting events, where there may be a risk of violence breaking out between opposing groups of protestors/fans (e.g., during football games). In this case, it would be important for law enforcement officials who are monitoring these situations from afar (e.g., via camera feed) to know exactly who has been involved in any disturbances so they can take appropriate action against them later on if necessary (e.g., arrest). This application could also help solve crimes faster since police officers wouldn't have time waste looking through hours worth of footage just trying to figure out which person committed said crime; instead, all they'd need to do was look up one database containing all known criminals' faces along with their corresponding names/IDs then cross reference those IDs against another database containing images taken during past incidents involving those particular individuals--a process which takes only seconds rather than minutes! Best Practices for Implementing Face Recognition Machine Learning ModelBest Practices for Implementing Face Recognition Machine Learning Model
- Data collection:
- Data pre-processing:
- Model selection:
The future of face recognition machine learning models is bright. The technology has already been integrated with other technologies, and the accuracy of these models is improving every day. There are many use cases for this technology that we haven't even thought about yet! How to Choose the Right Face Recognition Machine Learning Model
- Before you start building your face recognition model, it's important to identify your use case and assess its accuracy requirements.
- What is the purpose of this model? Is it for personal or commercial use? Do you need to identify people in real-time or only after they've been captured on video or photo?
- How accurate do you need it to be? In other words, how close does the system need to get before you can say "yes" with confidence that two faces belong together (or not)?
- Face recognition models vary significantly in terms of their accuracy requirements and cost/scalability tradeoffs. You must choose one that fits with your project goals so that neither money nor time goes wasted on unnecessary features.
In this article, we have discussed the basics of face recognition machine learning models. We have also discussed their applications and limitations. The main advantage of these models is that they can be used for various applications such as identifying people in a crowd or recognizing them from images captured by cameras at airports or banks etc., The main challenges faced by these models are that they require large amounts of data to train them and also need high-quality images for accurate results.