The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.

A lightweight Chrome extension that captures your TradingView chart, sends it to a YOLOv8 model hosted on Hugging Face (FastAPI backend), and returns an annotated chart with detected patterns like Rising/Falling Channels, Head & Shoulders, Inverse Head & Shoulders, Double Top, and Double Bottom. Features: One-click capture of visible TradingView

It performs Facial recognition with high accuracy. This attendance project uses webcam to detect faces and records the attendance live in an excel sheet. In order to determine the distinctive aspects of the faces based on distance, convolutional neural networks are used. All you need to do is stand in front of the camera and your face is verified instantly in milliseconds, without recording the attendance more than once. Facial recognition systems are commonly used for verification and security purposes but the levels of accuracy are still being improved. Errors occurring in facial feature detection due to occlusions, pose and illumination changes can be compensated by the use of hog descriptors. The most reliable way to measure a face is by employing deep learning techniques. The final step is to train a classifier that can take in the measurements from a new test image and tells which known person is the closest match. A python based application is being developed to recognize faces in all conditions. We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.

Detect faces from Image by AWS Rekognition, Google Vision, Azure Computer Vision, Face++, OpenCV, Dlib, Pigo, Tensorflow

This project demonstrates real-time face detection along with age and gender prediction using OpenCV and pre-trained deep learning models. The webcam feed detects faces, draws bounding boxes around them, and annotates each face with the predicted age group and gender.

Detect faces in real-time using OpenCV and a webcam. This Python script overlays timestamps and dates on detected faces, captures images, and saves them for further analysis. Features include geolocation integration and live video display with annotated face rectangles.

This project enables real-time face attribute analysis directly from a webcam using DeepFace and Streamlit. It captures video frames, detects faces, and displays attributes such as age, gender, emotion, and race—live and annotated.

Utilizes existing software such as Snapchat’s Snap AR Snap Camera and Lens Builder to create a Video Conferencing Plugin using AR filters to enhance university ESL pronunciation training. Annotates face landmarks around lip area and detects tongue appearence to assist teaching.

This project performs real-time facial analysis using MediaPipe FaceMesh and OpenCV. It detects: Head pose (Up, Down, Left, Right, Forward) Mouth state (Closed, Slightly Open, Wide Open) Face mesh visualization with annotated landmarks The system runs on a standard webcam and processes frames in real time.

Developed an object and face detection system using YOLOv8 and OpenCV. The model detects multiple object classes in images, counts the number of persons detected, and applies Haar Cascade to locate faces within the person regions. The solution runs in Google Colab and visualizes results with annotated bounding boxes.

A Python tool to detect and mark faces in images. Uses OpenCV’s Haar cascade for face detection and Pillow to draw rectangles around faces. Supports opening images via file dialog, visualizing detected faces, and saving the annotated images in multiple formats. Provides both a terminal-based version and a Tkinter GUI

Flask application integrated with OpenCV for real-time face detection. The application captures live video from the user's camera, processes it using OpenCV to detect faces, and streams the video with annotated face detection back to the client via Socket.IO.

Detect active handheld phone usage in videos (phone in hand, on lap, or near face) and generate an annotated video with bounding boxes, confidence labels, and preserved audio.

This repository contains a fine-tuned YOLOv11 model (yolo11n.pt) for detecting two classes: Cigarette and Face. The model has been trained using a custom dataset created in Roboflow, which includes labeled images to detect individuals smoking (cigarette) and faces. The dataset used for fine-tuning consists of annotated images.

"Face Detection using Deep Learning" uses VGG16 architecture and a self-defined loss function for regression, with Albumentations for data augmentation. LabelMe annotates images with bounding boxes for training. It detects faces in real-time from live video streams and applies bounding boxes, useful for security, video conferencing, and gaming.

A project using OpenCV and transfer learning of CNNs to detect faces and eyeglasses if one is wearing them. The dataset consists of 260 images annotated manually using makesense.ai, and is split into two classes - glasses and no glasses.

Underwater Image Enhancement (UIE) faces light absorption, scattering, fluorescence, and poor illumination. We propose (i) SDUI-9, an annotated dataset of nine degradations, and (ii) DARE-UI, a recursive framework where a classifier detects dominant degradations and iteratively applies targeted enhancement until restoration.

Working ---> After logging in, users can choose Emotion, Activity, or Random Playlist options, with Day/Night session settings. We trained a face recognition module in Jupyter, annotated gym and running videos, and created personalized playlists using YouTube URLs. Based on detected emotion and activity, the corresponding song plays automatically.

Displays the number of detected faces and annotated image.

Simple Face Detector IOS app to detect multiple human face from an image. It can detect multiple faces and also annotate the detected faces with rectangle .

A Python project utilizing OpenCV to detect faces and annotate eye regions in images using Haar cascade classifiers.

A Python-based AI face tracking system that detects and recognizes faces in videos using face_recognition and OpenCV. Automatically tags known faces, saves annotated frames, and generates a labelled video.

Detects face mesh landmarks on static images using Mediapipe and OpenCV in Python. Visualizes annotated face mesh, prints landmark coordinates, and provides clear setup and usage instructions.

This Python script uses MTCNN for face detection from either a live webcam stream or an image file. For webcam input, it continuously captures frames, detects faces, and annotates them with bounding boxes and facial keypoints. For image input, it detects faces in the image and provides bounding boxes and a count of detected faces.

End-to-end human image analysis pipeline using Python and AI – detect faces, emotions, posture, and clothing traits with annotated visual output.

A Python-based Face Recognition system that can identify faces in images or videos. Upload a group photo and a reference solo image to detect whether the person is present in the group. Can also count faces and display annotated results.

A Python-based Face Recognition system that can identify faces in images or videos. Upload a group photo and a reference solo image to detect whether the person is present in the group. Can also count faces and display annotated results.

A Python-based Face Recognition system that can identify faces in images or videos. Upload a group photo and a reference solo image to detect whether the person is present in the group. Can also count faces and display annotated results.

This Python project performs real-time facial detection and emotion recognition using OpenCV, MediaPipe, and FER, capturing video from an IP camera and annotating detected faces and emotions.

This code integrates real-time object detection using YOLO and face recognition with the face_recognition library. It captures video frames, detects objects and known faces, and annotates them with bounding boxes and labels. Detected object labels are converted to speech using pyttsx3. The video processing loop runs until 'q' is pressed to quit.