OpenCV is the huge open-source library for computer vision, machine learning, and image processing and now it plays a major role in real-time operation which is very important in today’s systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human. Object Detection is a computer technology related to computer vision, image processing, and deep learning that deals with detecting instances of objects in images and videos. We will do object detection in this article using something known as haar cascades.

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Real-time pose detection with customizable face overlays powered by Python 3.10, OpenCV, and MediaPipe.

This project explores real-time facial expression analysis using computer vision. It includes two versions: 🔹 A classic implementation using OpenCV and Haar cascades for face and smile detection. 🔹 An advanced version powered by a pre-trained Convolutional Neural Network (CNN) for real-time emotion classification.

Real-time face detection and pixelation using OpenCV and Haar cascades. A beginner-friendly image processing project that anonymizes faces captured by webcam video stream.

This project implements real-time face detection using OpenCV library in C++ programming language. The project uses pre-trained Haar cascades classifiers to detect faces in real-time video stream captured by the webcam.

This Repo contains Facial-Landmark-Detection using opencv ,numpy and dlib . In this Project, I have implemented Facial landmarks(key points) detection system using Convolution Neural network and image processing techniques. Facial landmark detection is regression kind of task where output is a set of values representing positions in the image . This project uses opencv , numpy and dlib libraries. I had used pre-trained model for the landmarks detection. For any task of processing facial features on real-time images, first step will be detecing the faces in the image. Face detection task is achieved by using dlib's implementation. Once faces are detected, we will feed them to the trained model to predict the landmarks.

Facial-Expression-Detection . Facial Expression or Facial Emotion Detector can be used to know whether a person is sad, happy, angry and so on only through his/her face. This Repository can be used to carry out such a task. It uses your WebCamera and then identifies your expression in Real Time. Yeah in real-time! PLAN This is a three step process. In the first, we load the XML file for detecting the presence of faces and then we retrain our network with our image on five diffrent categories. DEPENDENCIES Hit the following in CMD/Terminal if you don't have already them installed: pip install tensorflow pip install opencv-python That's it for now. So let's take a brief look at each step. STEP 1 - Implementation of OpenCV HAAR CASCADES I'm using the "Frontal Face Alt" Classifier for detecting the presence of Face in the WebCam. This file is included with this repository. You can find the other classifiers here. Next, we have the task to load this file, which can be found in the label.py program. E.g.: # We load the xml file classifier = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml') Now everything can be set with the emoji.py Program. So let's move to the next Step. STEP 2 - ReTraining the Network - Tensorflow Image Classifier We are going to create an Image classifier that identifies whether a person is sad, happy and so on and then show this text on the OpenCV Window. This step will consist of several sub steps: We need to first create a directory named images. In this directory, create five or six sub directories with names like Happy, Sad, Angry, Calm and Neutral. You can add more than this. Now fill these directories with respective images by downloading them from the Internet. E.g., In "Happy" directory, fill only those images of person who are happy. Now run the "train.py" program as suggested in the video Once you have only cleaned images, you are ready to retrain the network. For this purpose I'm using Mobilenet Model which is quite fast and accurate. To run the training, hit the got to the parent folder and open CMD/Terminal here and hit the following That's it for this Step. STEP 3 - Importing the ReTrained Model and Setting Everything Up Finally, I've put everything under the "train.py" file from where you can get everything. Now run the "emoji.py" program by typing the following in CMD/Terminal: python label.py It'll open a new window of OpenCV and then identifies your Facial Expression. We are done now! PLEASE DO STAR THIS REPO IF YOU FOUND SOMETHING INTERESTING. <3

# Real-time-Face-Detection-by-OpenCV This project is done using Pycharm IDE and Python, it is a Real-Time Face recognition using OpenCV while performing object detection using Haar feature-based cascade classifiers for detecting face, eyes, and smile. # 1. Import and initialize Start by importing OpenCV and create a directory (ex: Cascades) to gather all Haar classifiers files that you want to use in you project, then use their path to load them into your project. import cv2 faceCascade = "cv2.CascadeClassifier('Cascades/haarcascades/HAARCASCADE_FRONTALFACE_DEFAULT.xml') # 2. Setting up your camera To start we need to capture the face and to do so we are using the PC embedded camera which we are referring to it using (0) & setting the window size to specific measures in the following code lines: cap = cv2.VideoCapture(0) # write 0 to use the pc embedded camera cap.set(3,640) # set Width cap.set(4,380) # set Height # 3. Call the classifier function We will set our camera and inside the loop, load our input video in grayscale mode then we must call our classifier function, passing it some very important parameters, as scale factor, number of neighbors and minimum size of the detected face. while True: ret, img = cap.read() gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = faceCascade.detectMultiScale( gray, scaleFactor=1.2, minNeighbors=5, minSize=(20, 20) ) # 4. Detecting Faces The function will detect faces on the image. Next, we must "mark" the faces in the image, using, for example, a blue rectangle. If faces are found, it returns the positions of detected faces as a rectangle with the left up corner (x,y) and having "w" as its Width and "h" as its Height ==> (x,y,w,h). This is done with this portion of the code: for face in faces: top, right, bottom, left = face # draw a blue rectangle around the face cv2.rectangle(img, (top, right), (top + bottom, right + left), (255, 0, 0), 2) roi_gray = gray[right:right + left, top:top + bottom] roi_color = img[right:right + left, top:top + bottom] # 5. Final Touches If the user wants to quit the program, the button ESC is set to terminate the program in the following code lines, and to alert the user there is a small message shown on the top-right corner of the detected face boarders. k = cv2.waitKey(30) & 0xff if k == 27: # press 'ESC' to quit break cap.release() cv2.destroyAllWindows()

I intend to Implement a real-time Face detection and tracking the head poses position from high definition video using Haar Classifier through Raspberry Pi. SimpleCV and OpenCV libraries are used for face detection and tracking the head poses position. The experimental result computed by using computer vision SimpleCV and OpenCV framework libraries along with above mentioned hardware results were obtained through of 30 fps under 1080p resolutions for higher accuracy and speediness for face detection and tracking the head poses position.

Real-time face mask detection using deep learning and OpenCV. Powered by MobileNetV2 and built for public safety during pandemics. 🚀😷

A real-time attendance system using face recognition built with Python. The project detects faces from live video feeds, recognizes them by comparing with pre-encoded images, and logs attendance based on successful matches. It uses OpenCV for video capture and image processing, and the face_recognition library for face detection and encoding.

This project uses OpenCV and MediaPipe to detect facial landmarks and estimate head pose in real-time. The extracted data is processed by a neural network model to determine if a person is attentive. Features include real-time face mesh detection, pose estimation, and attention analysis.

This project is a real-time face mask detection system built using TensorFlow/Keras and OpenCV. It detects whether a person is wearing a face mask by using a trained Convolutional Neural Network (CNN). The system is designed for use in public safety applications such as entrance monitoring, smart surveillance, and healthcare facilities.

Face Recognition System Uses for multi Face Detection. It is a Real time Detection System that can be Easily Port on any Hardware Devices due to its written in python. The Model First Train the Faces and after that Predict the output with more than 85%+ confidence due to its model classifier training with good algorithms so that predict with more accuracy by the help of OpenCV Module Haarcascade classifier and using numpy and pandas to fiIt is a Multi nd an pixel array of images and put operations over there.

Click here to download the source code to this post In this tutorial, you will learn how to train a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning. Last month, I authored a blog post on detecting COVID-19 in X-ray images using deep learning. Readers really enjoyed learning from the timely, practical application of that tutorial, so today we are going to look at another COVID-related application of computer vision, this one on detecting face masks with OpenCV and Keras/TensorFlow. I was inspired to author this tutorial after: Receiving numerous requests from PyImageSearch readers asking that I write such a blog post Seeing others implement their own solutions (my favorite being Prajna Bhandary’s, which we are going to build from today) If deployed correctly, the COVID-19 mask detector we’re building here today could potentially be used to help ensure your safety and the safety of others (but I’ll leave that to the medical professionals to decide on, implement, and distribute in the wild). To learn how to create a COVID-19 face mask detector with OpenCV, Keras/TensorFlow, and Deep Learning, just keep reading! Looking for the source code to this post? JUMP RIGHT TO THE DOWNLOADS SECTION COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning In this tutorial, we’ll discuss our two-phase COVID-19 face mask detector, detailing how our computer vision/deep learning pipeline will be implemented. From there, we’ll review the dataset we’ll be using to train our custom face mask detector. I’ll then show you how to implement a Python script to train a face mask detector on our dataset using Keras and TensorFlow. We’ll use this Python script to train a face mask detector and review the results. Given the trained COVID-19 face mask detector, we’ll proceed to implement two more additional Python scripts used to: Detect COVID-19 face masks in images Detect face masks in real-time video streams We’ll wrap up the post by looking at the results of applying our face mask detector. I’ll also provide some additional suggestions for further improvement. Two-phase COVID-19 face mask detector Figure 1: Phases and individual steps for building a COVID-19 face mask detector with computer vision and deep learning using Python, OpenCV, and TensorFlow/Keras. In order to train a custom face mask detector, we need to break our project into two distinct phases, each with its own respective sub-steps (as shown by Figure 1 above): Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask We’ll review each of these phases and associated subsets in detail, but in the meantime, let’s take a look at the dataset we’ll be using to train our COVID-19 face mask detector.

This project is done using Pycharm IDE and Python, it is a Real-Time Face recognition using OpenCV while performing object detection using Haar feature-based cascade classifiers for detecting face, eyes, and smile.

Real time face detection by OpenCv

Face Detection using OpenCV in Python. Detects and highlights faces in real-time video from webcam using Haar Cascade classifier. Displays grayscale video with detected faces outlined by rectangles. Requires Python and OpenCV.

This project is powered by OpenCV and Face Recognition libraries, designed to automate attendance through face detection. This repository includes the codebase for real-time face recognition, finding encodings and a real-time database that dynamically updates attendance.

This project implements a real-time face detection system using Python and OpenCV. It processes video input frame by frame and detects human faces using a pre-trained Haar Cascade classifier.

The real-time face landmark detection system in Python was developed using the OpenCV library. Face detection is a computer technology used in a variety of applications to identify human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene.

Designing and implementing a real-time face recognition system using Deep Learning (FaceNet) and OpenCV, enabling secure fintech transactions with facial authentication, improving fraud detection accuracy by 95%.

Real time Computer vision Projects done by OpenCV library. It has several uses like Object Detection, Face Recognition, Detection, image Processing and Video Processing. Computer Vision overlaps with fields like Image Processing, Photogrammetry, and Pattern Recognition

This project aims to track mental health status using real-time face detection and emotion analysis. By leveraging Flask, OpenCV, and MySQL, the system captures live video, detects faces, and provides suggestions based on the detected emotion.

This project is a real-time Face Recognition System built using OpenCV and Python, leveraging the haarcascade_frontalface_default.xml classifier for efficient face detection. The system detects human faces from live webcam input or images, preprocesses them, and enables recognition by comparing facial features.

Smile-CNN is a real-time face and smile detection project made using Flask, Tailwind CSS, OpenCV, AJAX and a trained CNN. It was done during the ML internship conducted by IIT Bhubaneswar.

A real-time detection and recognition app inspired by Gundam's ZERO System. It uses OpenCV and machine learning to detect faces, recognize objects, and understand surroundings. Modular and extensible, it supports custom-trained models for advanced tasks like robotic face detection and scene classification. Built for dynamic intelligence.

This project utilizes OpenCV for real-time face detection and streams the processed video through a virtual camera. The virtual camera output can be used by any application that supports video input, such as video conferencing or streaming software.

A facial recognition-based Smart Attendance System using Python, OpenCV, Flask, and Google Sheets. Features real-time face detection, admin dashboard, shortage tracking, and dual attendance storage (Excel + Google Sheets). Developed by Anusha Devadiga and team for academic use.