Face recognition using python is a technique of identifying or verifying the identity of an individual using their face. There are numerous algorithms that may do face recognition however their accuracy would possibly range. Here I’m going to describe how we do face recognition utilizing deep learning.
So now allow us to perceive how we recognise faces utilizing deep studying. We make use of face embedding through which every face is transformed right into a vector and this method known as deep metric studying. Let me further divide this process into three easy steps for easy understanding:
Face Detection: The very first process we carry out is detecting faces within the picture or video stream. Now that we know the exact location/coordinates of face, we extract this face for further processing ahead.
What is OpenCV?
OpenCV (Open Source Computer Vision Library) is a library of programming functions mainly aimed at real-time computer vision. It was originally developed by Intel and is now maintained by a non-profit foundation. It is written in C++ and has interfaces for C++, Python, and Java. OpenCV supports many image processing and computer vision techniques and is widely used in research and industry. Some of its features include object detection, image recognition, face detection, and image manipulation.
OpenCV has a wide range of features and capabilities that make it a powerful tool for image and video processing. Some of its key features include:
- Image processing: OpenCV provides a variety of image processing functions, such as filtering, morphological operations, color space conversion, and histogram manipulation.
- Object detection: OpenCV includes pre-trained classifiers for object detection, such as Haar cascades and HOG + SVM detectors, as well as the ability to train custom classifiers.
- Image recognition: OpenCV includes functionality for feature extraction, such as SIFT, SURF, and ORB, as well as machine learning techniques for image recognition such as SVM, KNN and Random Forest.
- Video analysis: OpenCV has functions for motion analysis, object tracking, and background subtraction, as well as support for video file formats and cameras.
- Camera calibration and 3D reconstruction: OpenCV includes functions for camera calibration and stereo vision, which can be used for 3D reconstruction of real-world scenes.
- Deep Learning integration: OpenCV also provides integration with deep learning frameworks such as TensorFlow and Caffe, which allow for even more powerful image processing and object detection capabilities.
OpenCV is open-source and cross-platform and can be used on Windows, Linux, and macOS. It also has a large and active community, which contributes to the development of the library and provides support for users
Advantages of OpenCV:
- OpenCV is an open-source library and is free of cost.
- As compared to other libraries, it is fast since it is written in C/C++.
- It works better on System with lesser RAM
- To supports most of the Operating Systems such as Windows, Linux and MacOS
Few Steps to Creating a Face Recognition Using Python:
1. Install the necessary libraries such as OpenCV, dlib, and imutils by running the following commands in your terminal:
pip install opencv-python pip install dlib pip install imutils
2. Acquire a pre-trained facial landmark detector by downloading a model file such as the one provided by dlib or by training your own detector using a dataset of facial images.
3. Create a new Python file and import the necessary libraries:
import cv2 import dlib
4. Load the pre-trained detector by instantiating a
dlib.get_frontal_face_detector() object and create a function to detect faces in images or video frames.
detector = dlib.get_frontal_face_detector() def detect_faces(image): gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) faces = detector(gray, 1) return faces
5. Use OpenCV to draw a bounding box around the detected face and display the image or frame with the bounding box:
def draw_bounding_box(image, faces): for face in faces: x1, y1, x2, y2, w, h = (face.left(), face.top(), face.right(), face.bottom(), face.width(), face.height()) cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.imshow("Faces", image) cv2.waitKey(0)
6.Create a GUI using a library such as Tkinter to display the images or frames with the bounding boxes in a window.
You can use a webcam or a video file as input to the application.
Please note that this is just a basic example and additional steps may be required depending on your specific application and requirements.