335 skills found · Page 11 of 12
joshiadinath0 / Face Detection Library SystemA library management system using python which uses face recognition to login users. Book are given a special qr code and are scanned with camera to log them into the database.
sumitrathore1313 / FaceRecognition For RaspberryPiIt is python face recognition system. which is specially developed for raspberry pi.
PT-Perkasa-Pilar-Utama / Ppu UnifaceA type-safe, opinionated port of python's uniface: A Comprehensive Library for Face Detection, Recognition
adneovrebo / Face Recognition Door Opener Raspberry PiPython face recognition door opener with simple GUI interface built with Raspberry Pi 3, PiCamera and servo.
sumitjhaleriya / AutoSelfieThis Python script utilizes OpenCV and Pyttsx3 to create a real-time selfie capture application with facial recognition features. It captures a selfie when a smile is detected on a recognized face, and the user is notified through spoken feedback.
grimmerk / Electron Face FinderUse a image including a face to find out the other images including similar faces. Stack: Docker, Electron (Desktop Application), Python Websocket Server (git submodule) + Face recognition Trained NN (neural network) Model.
Prashant44-cell / Face Recognition Try1Trying to build face recognition system using python based tools and models that are capable to build this system.
Ananya-98 / Face Recognition In Keras Using CNNFace recognition system is implemented in python 3.7 using keras and opencv. CNN model is used for prediction of the test image.
SyedHassanUlHaq / Face RecognizerFace Recognizer is a Python-based facial recognition system developed using OpenCV. This project aims to provide an efficient and easy-to-use tool for facial detection and recognition in real-time.
fvlima / RaspycammonitorA simple and experimental python script to run on raspberry, that uses picamera and opencv-python to detect human presence by face recognition, taking a photo and sending it through email
Abasy714 / Emotion DetectionReal-time multi-face emotion detection using YOLOv8 and OpenCV. Detects facial expressions like happy, sad, angry, and more with color-coded bounding boxes and live webcam support. Built with Python, Ultralytics YOLO, and face_recognition.
rahul-2004-json / AttendifyAttendify is a web-based AI attendance system that uses facial recognition to automate tracking. Teachers upload classroom photos, and the system detects and matches student faces with the database to mark attendance. Built with Python, OpenCV, React.js, and MySQL/MongoDB, it saves time, reduces errors, and improves efficiency.
10abdulmoid / Facerecognition CliFacerecognition-cli- is a Python-based command-line interface (CLI) tool designed for facial recognition tasks. It allows users to detect and recognize faces in images or video streams directly from the terminal. The project leverages popular machine learning libraries and computer vision techniques to provide accurate and efficient face detection
eric-erki / Openpose Based GUI For Realtime Pose Estimate And Action RecognitionGUI based on the python api of openpose in windows using cuda10 and cudnn7. Support body , hand, face keypoints estimation and data saving. Realtime gesture recognition is realized through two-layer neural network based on the skeleton collected from the gui.
monster8d / Face Recognition Attendance SystemPython Coded Script able to Take attendence Via Face Recognition using your Webcam and make them markable as an Existing user for the next time while you click on take Attendence, also give your attendence in Excel (CSV) format and captures all the photos in Training folder.
Mayank-MP05 / Marathi Letter Recognition ConvNetMarathi-Letter-Recognition-ConvNet This project is Deep Learning Web Interfaced character recognition project. For sake of simplicity flask backend is used to minimize complexities. Basic working include you can draw the character on the canvas and system will detect the character. Tools used : Jupyter Notebooks - Model Building , Data analysis , Exporting model Flask - Python Backend library for Simple Backend with python integrations Bootstrap - Clean Looking Frontend Web face DrawingBoard.js - Used to Build the Drawable Canvas element Javascript - Lightwieght JS used for DOM manipulation
luisgarzac / Data Science Course Udemy Frogames Juan Gabriel GomilaData Science course taught by Juan Gabriel Gomila. Part 1 - Installing Python and packages needed for data science, machine learning, and data visualization Part 2 - Historical evolution of predictive analytics and machine learning Part 3 - Pre-processing and data cleaning Part 4 - Data handling and data wrangling, operations with datasets and most famous probability distributions Part 5 - Review of basic statistics, confidence intervals, hypothesis tests, correlation,... Part 6 - Simple linear regression, multiple linear regression and polynomial regression, categorical variables and treatment of outliers. Part 7 - Classification with logistic regression, maximum likelihood estimation, cross validation, K-fold cross validation, ROC curves Part 8 - Clustering, K-means, K-medoids, dendrograms and hierarchical clustering, elbow technique and silhouette analysis Part 9 - Classification with trees, random forests, pruning techniques, entropy, information maximization Part 10 - Support Vector Machines for Classification and Regression Issues, Nonlinear Kernels, Face Recognition (How CSI Works) Part 11 - K Nearest Neighbors, Majority Decision, Programming Machine Learning Algorithms vs Python Libraries Part 12 - Principal Component Analysis, Dimension Reduction, LDA Part 13 - Deep learning, Reinforcement Learning, Artificial and convolutional neural networks and Tensor Flow
AsmitaBarman / Face Recognition Door Unlock RaspberryActual Project file https://drive.google.com/file/d/1RCJ271K1B5Ig839c_0UCq8oWn5mpz7EN/view?usp=sharing Introduction This project was part of the embedded system design course, and uses face recognition to control a servo lock. The face recognition has been done using the Eigenfaces algorithm (Principle Component Analysis or PCA) and implemented using the Python API of OpenCV. Open Source Project source It's a slight modification of the Raspberry Pi Face Recognition Treasure Box project by Tony Dicola on the Adafruit Learning System. The code has been modified at places to replace the use of the RPIO library (which has issues running on the new Raspberry Pi 2 Model B+) with the standard RPi.GPIO library. The project has also been implemented to work as an automated home lock system which unlocks for the owner of the house and doesn't for any other visitor. It also plays an appropriate voice message. IMPLEMENTATION DETAILS This slight modification also changed the way of installing the dependencies,OpenCV & Python version and also the installation of updated GPIO ports for Raspberry B+. The modifications that has done here also includes the .wave sound files that tends to start or stop depending upon the door recognition status. OpenCV Installation This project depends on the OpenCV computer vision library to perform the face detection and recognition. Unfortunately the current binary version of OpenCV available to install in the Raspbian operating system through apt-get (version 2.3.x) is too old to contain the face recognition algorithms used by this project. However you can download, compile, and install a later version of OpenCV to access the face recognition algorithms. Note: Compiling OpenCV on the Raspberry Pi will take about 3 hours of mostly unattended time. Make sure you have some time to start the process before proceeding. First you will need to install OpenCV dependencies before you can compile the code. Connect to your Raspberry Pi in a terminal session and execute the following command: sudo apt-get update sudo apt-get install build-essential cmake pkg-config python-dev libgtk2.0-dev libgtk2.0 zlib1g-dev libpng-dev libjpeg-dev libtiff-dev libjasper-dev libavcodec-dev swig unzip Answer yes to any questions about proceeding and wait for the libraries and dependencies to be installed. You can ignore messages about packages which are already installed. Next you should download and unpack the OpenCV source code by executing the following commands: wget http://downloads.sourceforge.net/project/opencvlibrary/opencv-unix/2.4.10/opencv-2.4.10.zip unzip opencv-2.4.10.zip Note that this project was written using OpenCV 2.4.10, although any 2.4.x version of OpenCV should have the necessary face recognition algorithms. Now change to the directory with the OpenCV source and execute the following cmake command to build the makefile for the project. Note that some of the parameters passed in to the cmake command will disable compiling performance tests and GPU accelerated algorithms in OpenCV. I found removing these from the OpenCV build was necessary to help reduce the compilation time, and successfully compile the project with the low memory available to the Raspberry Pi. cd opencv-2.4.9 cmake -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local -DBUILD_PERF_TESTS=OFF -DBUILD_opencv_gpu=OFF -DBUILD_opencv_ocl=OFF After this command executes you should see details about the build environment and finally a '-- Build files have been written to: ...' message. You might see a warning that the source directory is the same as the binary directory--this warning can be ignored (most cmake projects build inside a subdirectory of the source, but for some reason I couldn't get this to work with OpenCV and built it inside the source directory instead). If you see any other error or warning, make sure the dependencies above were installed and try executing the cmake command again. Next, compile the project by executing: make This process will take a significant amount of time (about 3 hours), but you can leave it unattended as the code compiles. Finally, once compilation is complete you can install the compiled OpenCV libraries by executing: sudo make install After this step the latest version of OpenCV should be installed on your Raspberry Pi. Python Dependencies The code for this project is written in python and has a few dependencies that must be installed. Once connected to your Raspberry Pi in a terminal session, execute the following commands: sudo apt-get install python-pip sudo apt-get install python-dev sudo pip install picamera sudo pip install RPi.GPIO You can ignore any messages about packages which are already installed or up to date. These commands will install the picamera library for access to the Raspberry Pi camera, and the GPIO library for access to the Pi GPIO pins and PWM support. Hardware The Hardware required for this project are as follows: Raspberry Pi ( I prefer Model 2 B+) Raspberry Pi Camera Micro Servo One Push Button Power Supply for the Servo (5V Source) One 10K resistor for pull down Breadboard and Jumper wires for connections The necessary circuit diagrams and further explanations are explained in depth in the original pdf accompanying the project. Kindly go through it first.
laiguixian / Facerec中文：这个是架设人脸识别服务的整个的源代码，为了便于理解，已经做了简化处理，删除了各种商业化的技术，你可以自己根据需要加入商业化的技术，比如BS系统部分你可以加入数据库，比如mysql，使用SSM（spring,springmvc,mybatis）框架，springboot，MQ等等，前端比如使用elementui,layui等等，python部分你也可以加入各种东西，总之看需要加入自己需要的东西。模型需要自己去找照片来训练，本来想放上来的，但是文件太大传不了，所以只能你们自己去训练模型，可以用官方的照片集来训练，由于文件数量等问题无法上传，other.rar和tracking.rar都需要自己解压出来。包含一个BS的展示系统（前端：H5，js，jquery，ajax，css，tracking.js等，后台：java+JSP+redis，以webservice提供服务）和提供人脸识别的服务，这个服务使用python开发，基于facenet，这个系统是我多年前（约2018年）自己基于facenet开发的，现在把源代码公开出来，由于暂时没有时间，所以直接公开，未做代码调整和优化，希望对技术传播有所助益。本代码公开纯属技术交流，没有收取任何费用。 English:This is the entire source code for setting up face recognition services.In order to make it easier to understand, we have simplified and deleted various commercialized technologies. You can add commercialized technologies according to your needs. For example, in the BS system, you can add databases, such as mysql, using SSM (spring, springmvc, mybatis) framework, springboot, MQ, etc. in the front end, such as elementui, layui, etc. in the python part, you can also add various things, In short, we need to add what we need.The model needs to find photos to train by yourself. I originally wanted to put them on, but the file is too large to transmit, so you can only train the model by yourself. You can use the official photo collection to train.Due to the number of files and other problems, it is impossible to upload. other.rar and tracking.rar need to extract them by yourselves. It includes a BS system (front-end: H5, JS, jQuery, AJAX, CSS, tracking.js, etc., back-end: java,jsp,redis, etc, which provides services with WebService )and provides face recognition services. This service is developed using Python and based on facenet. This system was developed by myself based on facenet many years ago (about 2018). Now the source code is open. Because there is no time for now, it is directly open, The code has not been adjusted and optimized, and I hope it will be helpful to the dissemination of technology. The disclosure of this code is purely technical exchange, and no fee is charged.
SouthWind017 / FaceRecognitionA simplest Python camera face recognition
