A Guide to Face Detection in Python

Description:-

Face detection in Python is a technique used in computer vision to locate and identify human faces in digital images. It is typically used in applications such as facial recognition, security and surveillance, digital photography, and social media. Face detection algorithms analyze an image or video frame and detect any faces present in the frame. They typically return a bounding box around the face and associated information such as the location, size, and confidence score.

Face recognition identifies a face in a photo or a video image against a pre-existing database of faces. It uses a facial recognition algorithm to compare a facial image with a collection of stored facial images and works to match the right face with its identity. If a match is found, the identity of the person is then established.

Face recognition technology has a wide range of applications, such as security, criminal investigation, and advertising. It can also be used to identify people in photos, videos, or live streams.

Facial Detection in Python can be done by following a few simple steps.

(I) Dataset Collection

(II) Train a Model

(III) Transform Classifier into Detector

(I) Dataset Collection: Collect Train and Test Data from User By OpenCV Python File

First of all, we need to collect a dataset. I used my pictures and pictures of my friends to train the model, but you can also train the model on some other face datasets. I used only personal photos to check whether such a small dataset is enough for the model to show acceptable quality.

I cropped the face from each photo and resized them to the size of 600×600 (which you don’t have to do, since TensorFlow can do it for you). Here is the

stepwise guide to the entire process.

Step 1. Import necessary libraries. Also, define the haar cascade to detect the face of the user.

Step 2. Define a function to extract the face from the captured image.

Step 3. Capture training data set

Now, define a webcam recorder with a count =0 variable. After this, create a while loop to capture the image till the count value does not reach 100.

Step 4. Now, release the video capture object.

(II) Train a Model: Develop a face recognition model for face identification and verification using the pre-trained VGGFace2 deep learning model.

Step 1. Make a new Python file & install keras, golb library.

keras libray: keras is a deep learning api written in python, running on top of the machine learning platform tensorflow. it was developed with a focus on enabling fast experimentation. Being able to go from idea to result as fast as possible is key to doing good research.

keras is:

Simple — But not simplistic. keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter.

Flexible — keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon what you’ve already learned.

Powerful — keras provides industry-strength performance and scalability: it is used by organizations and companies including NASA, YouTube, and Waymo.

Command:

Step 2. Import necessary libraries function and classes.

Step 3. Define image size with train/test-path.

Step 4. Add a pre-processing layer to the front of vgg. Here we use imagenet.

Step 5. Find how many folders we have in the train & test dataset.

Step 6. Define the model.

Step 7. Define compile parameters to the models.

Step 8. Fit data set with the model.

Step 9. Before deploying and using the model, find the accuracy and loss in the model.

Note: With this last step our model is ready to face detection.

(III) Transform Classifier into Detector: Create a new python file for detecting face with the computer camera with help of a trained model

Step 1. Import necessary libraries.

Step 2. Load model by load_model function.

Step 3. Load haar cascade to classify faces.

Step 4. Define face_extractor function to crop images from the webcam.

Step 5. Predict person face from trained model.

Initialize the webcam to read faces from the camera. after extracting the face by face extractor function. Also add an extracted image with model to find the predicted values.

Step 6. Use a conditional loop to find out the image name from our data set.

Output: GIF

Conclusion:

With this, we have come to the end of this tutorial. To sum up, face detection in Python is a powerful tool for automating the process of recognizing human faces in digital images. It can be used for a variety of tasks such as facial recognition, facial classification, and facial analytics. In this tutorial, we learnt how to use libraries such as OpenCV and implement face detection in Python easily. Hope you enjoyed the tutorial and understood the process of face detection in Python.

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