Face recognition system

Real Time Multiple Cameras Face Recognition SystemThis project aims to build a real-time face recognition system that can capture video streams from multiple cameras using RTSP protocol, analyze the video frames to detect faces, create bounding boxes around those faces and labeling thoses boxes with the person names.Demonstration Environment:CPU: Intel i3 11th gen @ 3.00GHz 2 cores 4 threads face.online-video-cutter.com.mp4 Development PipelineThe development pipeline for the face recognition system consists of several key steps, including model selection, data preprocessing, training, and deployment. The following is an overview of the pipeline:Capturing Video Frames from Multiple Cameras using RTSP Protocol:Efficient capture of video frames from multiple cameras is crucial for real-time face recognition.The system implements the RTSP protocol to access camera streams, which provides efficient video transmission over IP networks.Threading techniques are employed to capture frames from multiple cameras simultaneously, improving performance by alleviating heavy I/O operations to separate threads.By using threading, frames can be continuously read without impacting the performance of the main program.Model Selection:Initially, the system utilized the insight-face-paddle library developed by PaddlePaddle for face recognition.However, due to computational limitations on a CPU-based system, it was necessary to optimize the face detection model.Face Detection Model Optimization:The original face detection model, BlazeFace, was computationally expensive for continuous frame processing.As an alternative, the PyramidBox model was chosen as it demonstrated robustness against interferences and was optimized for mobile devices.The lightweight version of the PyramidBox model was preferred to ensure efficient operation on embedded systems and mobile devices.Integration of Face Detection and Recognition:The face detection model was integrated with the existing face recognition system from insight-face-paddle.When processing frames from a video stream, the face detection model detects faces and extracts face crop images.These face crop images are then passed through the face recognition model, MobileFace, to generate face embeddings.Similarity Measurement:The face embeddings obtained from the MobileFace face recognition model are used to compute the cosine similarity between the camera feed faces and provided image faces.This similarity measurement helps determine the degree of resemblance between faces, enabling face recognition and identification.Multithreading for Model Inference:To fully utilize available system resources, multithreading techniques were employed to handle the model inferencing operation.Multithreading ensures that both face detection and face recognition models can make predictions concurrently, optimizing system performance.GPU Support:Additionally, a script for GPU support was developed, enabling the system to leverage GPU acceleration if available.The GPU support script enhances the overall processing speed and allows for more efficient utilization of computational resources.Setup ProcessThis guide outlines the steps to set up the required Python environment and install the necessary packages for face recognition using PaddlePaddle. Follow the instructions below to get started:1. Create Python Virtual EnvironmentTo begin, create a Python virtual environment using Python 3.8. Make sure you have pip version 20.0.2 installed. Run the following commands:python3.8 -m venv myenv # Replace `myenv` with your preferred environment namesource myenv/bin/activate # Activate the virtual environment2. Install PaddlePaddle FrameworkNext, we need to install the PaddlePaddle framework. We'll be using version 2.4.2 with cpu support. Use the following command:python -m pip

The eight best face recognition attendance systems for work are Buddy Punch, ADP, Jibble, Timeero, Truein, AMGtime, Fareclock, and ClockShark. You can see a side-by-side comparison of each of these apps below, or continue reading for our in-depth reviews covering how each system works, what other features they offer, and how much they cost.PlatformHighlightsRatingPaid Plans Start AtBuddy PunchBest for affordability and ease of use4.8/5$4.49 per user per month plus a $19 per month base feeADP Workforce NowBest for large companies4.4/5Contact for pricingJibbleBest free option for small businesses4.9/5Free for facial recognition without spoofing preventionTimeeroBest for employee flexibility4.4/5$11 per user per monthTrueinBest for healthcare4.8/5$2.50 per user per monthAMGtimeBest for on-site facial recognition kiosks4.1/5$391.50-$1995 per device plus $1 per user per monthFareclockBest for point-based attendance systems4.6/5Free plan with facial recognition for up to five usersClockSharkBest for field services businesses4.7/5$8 per employee per month plus a $40 per month base feeWhat is a face recognition attendance system?A face recognition attendance system is software that analyzes employees’ facial features in order to identify them and record their attendance. Employees use the system to take a photo of themselves when they want to record their attendance, and the system compares that photo to the others it has on record to validate the employee’s identity.In the workplace, a face recognition attendance system is likely to be the same system you use to track employees’ hours. Employees use the system to take a photo of themselves when they want to clock in or out, the system validates their identity, and if it’s a match, the employee is automatically clocked in or out.The benefits of using an attendance system with facial recognitionAttendance systems with facial recognition bring employers many benefits:They allow for quicker clocking in and out: Employees simply stand in front of the camera to clock in and out instead of typing in long usernames and passwords. This can prevent long lines from forming when employees all clock in and out at a central kiosk. They prevent time theft: Facial recognition makes buddy punching — where one employee clocks in/out for another — impossible. This can help you save on labor costs and improve the accuracy of your timesheets, attendance records, and payroll.They enhance your security: Some facial recognition systems can be used to allow employees into and out of restricted areas. This is significantly more secure than using keycards or badges that may be easily handed off to someone else.They help you make better decisions: Because these systems improve the accuracy of your attendance data, they can help you identify attendance issues more easily and make more data-driven decisions on things like staffing and scheduling.Features to look for in a face recognition attendance systemThere are several key

FR1000 Face, Fingerprint Biometric Recognition Technology | WIFIAMG FR-1000 Biometric Facial Recognition SystemAMG Face Recognition Technology, biometric time clock (FR-1000) combines state of the art biometric technology with secure, reliable data management.A complete time and attendance system, the FR-1000, instantly identifies employees and allows for a touch less hygienic alternative to fingerprint readers while still eliminating buddy punching.Here are some of the Face Recognition Technology benefits:Identification modes include facial recognition, fingerprint, proximity card/FOB and/PIN w/password.Elegant ergonomic design.Ethernet and WiFi communication.6 User-defined function keys. FR1000 Face, Fingerprint Biometric Recognition Technology | WIFIFeatures of AMG Face Recognition Technology:User ManagementEnroll, Delete and Edit UsersCommunication SettingsSet IP Address, Subnet Mask and GatewayData Management i.e. clear logs, users etc.Set function keysSet Display OptionsSet /date abd /tuneUSB Drive Data ManagementUpload and Download Data to USB DriveTest the HardwareView RecordsSystem InfoFR1000 Face, Fingerprint Biometric Recognition Technology | WIFISpecifications of AMG Face Recognition Technology:Maximum Face Employee Capacity: 200 Employees (per time clock)Maximum Fingerprint Employee Capacity: 1000 Employees (per time clock)Maximum Card Capacity: 10,000 Employees (per time clock)Verification Method: Facial recognition, fingerprint sensor, badge and PINVerification Time: Less than 1 secondConfirmation: Display, Led indicator and Voice confirmationLCD Display: Touch screen 2.8 inch TFT color LCD, 320 x 240 resolutionCommunication Interface: Built-in Ethernet and USB (cable/flash drive)Memory Retention: Up to 5 years via the standard internal lithium batterySize / Dimension: 167 x 147 x 34 mm(L x H x D)Weight: 1lbsPower: 5 Volt DC - 3 Watt (input:100 - 110 VAC) US adapter.Operating Environment: Operating : 32 to 113 Fahrenheit (0 to 45 Celsius)Working Humidity: 20% - 80%Installation Method: Wall Mounting FR1000 Face, Fingerprint Biometric Recognition Technology | WIFIPower sourceEthernet, serial or USB Flash Drive (Depending on communication of choice)

Here are 214 public repositories matching this topic... Code Issues Pull requests Discussions Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go. Updated Aug 12, 2024 Go Code Issues Pull requests JS Expert Week 7.0 - 🙅🤏🏻 Controlling Streaming Platforms using Eye and Hand Detection 👁🖐 Updated Dec 12, 2023 JavaScript Code Issues Pull requests 👀 Use machine learning in JavaScript to detect eye movements and build gaze-controlled experiences. Updated Jun 24, 2021 JavaScript Code Issues Pull requests Face recognition SDK Android with 3D passive liveness detection (Face Detection, Face Landmarks, Face Recognition, Face Liveness, Face Pose, Face Expression, Face attributes) Updated Mar 15, 2025 Java Code Issues Pull requests VideoView that plays video only when 👀 are open and 👦 is detected with various other features Updated Mar 28, 2023 Java Code Issues Pull requests Driver Drowsiness Detector detects if a driver or a person is drowsy or not, using their eye movements. Updated May 8, 2024 Python Code Issues Pull requests 📷 Object detection with OpenCV on Java. DNN, HaarCascade, Template Matching, Color Detection etc. Updated Mar 18, 2019 Java Code Issues Pull requests A simple 👀 tracking and 👦 detection android app Updated Jan 20, 2020 Java Code Issues Pull requests [TPAMI] Automatic Gaze Analysis ‘in-the-wild’: A Survey Updated May 29, 2024 Code Issues Pull requests Training scripts and data for a webcam-based, eye-tracking mouse Updated Nov 25, 2022 Python Code Issues Pull requests Auto Attendance System Using Real Time Face Recognition With Various Computer Vision & Machine Learning Tools Updated May 3, 2021 Java Code Issues Pull requests Face Recognition SDK Javascript using ONNX Runtime Web and OpenCV.js (Face Detection, Face Landmarks, Face Liveness, Face Pose, Face Expression, Eye Closeness, Age, Gender and Face Recognition) Updated Oct 8, 2024 JavaScript Code Issues Pull requests Detect eye blinks based on eye aspect ratio (EAR) introduced by Soukupová and Čech in their 2016 paper, Real-Time Eye Blink Detection Using Facial Landmarks. Updated Jul 16, 2019 Python Code Issues Pull requests Face recognition SDK iOS with 3D passive liveness detection (Face Detection, Face Landmarks, Face Recognition, Face Liveness, Face Pose, Face Expression, Face attributes) Updated Mar 15, 2025 Swift Code Issues Pull requests An experiment on gaze tracker system, based on OpenCV, which shows how to control mouse pointer using eyes and gaze estimation Updated Oct 3, 2023 Python Code Issues Pull requests A Realtime CPU eye detector to detect if the eyes are open or closed Updated Feb 23, 2023 Python Code Issues Pull requests Official implementation of the pupillometry system called PupilSense proposed in the article "PupilSense: Detection of Depressive Episodes Through Pupillary Response in the Wild". Updated Feb 26, 2025 Python Code Issues Pull requests Giriş Seviyesinden İleri Seviyeye Kadar Python ile Görüntü İşleme.. Nesne Tespiti, Nesne Takibi, Yüz Tespiti, Göz Algılama ve Göz Hareketleri Takibi, Beden Tespiti, El Hareketlerini Anlamlandırma, Şerit Takibi, Plaka Okuma vs. gibi resim, video ve gerçek zamanlı örnek uygulamalar Updated Dec 27, 2020 Python Code Issues

Distribution page. (c) Video viewing scene. Figure 9. Experimental tools and settings: (a) Intel RealSense Depth Camera D435. (b) Experimental settings with three distances. Figure 9. Experimental tools and settings: (a) Intel RealSense Depth Camera D435. (b) Experimental settings with three distances. Figure 10. Representative eye line mosaic effects using bone sensing and depth camera. Figure 10. Representative eye line mosaic effects using bone sensing and depth camera. Figure 11. Outputting feature positions from bone sensing and face recognition, respectively. Figure 11. Outputting feature positions from bone sensing and face recognition, respectively. Figure 12. Approaches using face recognition: (a) Eye line mosaic. (b) Face mosaic. Figure 12. Approaches using face recognition: (a) Eye line mosaic. (b) Face mosaic. Figure 13. Facial masking results using image-based bone sensing in this experiment. Figure 13. Facial masking results using image-based bone sensing in this experiment. Figure 14. Facial masking results using image-based face recognition in this experiment. Figure 14. Facial masking results using image-based face recognition in this experiment. Figure 15. Comparing area of masking face with different directions and distances. Figure 15. Comparing area of masking face with different directions and distances. Table 1. A comparison of related work for the face masking. Table 1. A comparison of related work for the face masking. Research Authors(Year)Masking TargetMaterials and MethodsIntrusivenessContributionsKim et al. [18] (2018)FaceCCTV cameras,video surveillance,face identificationExistFound optimal maskingvalues in case studyKim et al. [17] (2020)FaceClosed-circuit television,CCTV cameras,video surveillanceExistInserted a virtual faceused a blockchain asthe storage areaRajput et al. [19] (2020)FaceRGB-D sensors,deep CNN,cloud serviceNoneImproved thesecurity-recognitionaccuracyZhu et al. [15] (2020)Face,body key pointsDe-identification,keypoint preservation,face-swappingExistThough videoprocessing tode-identify subjectsOur research inthis paper (2022)Face,eye lineGeneral computer,bone sensing,edge computingExistValidated advantagesof bone sensingat multi-views Table 2. Technologies used in the implementation of the proposed system. Table 2. Technologies used in the implementation of the proposed system. ItemTechnologies and Libraries UsedHTTP serverexpress, httpWebSocket technologysocket.ioPeerJS technologypeerRandom ID generationuuidBasic authenticationbasic-auth-connectBone sensingPoseNet ModelWeb browserChrome (101.0.1951.54) Table 3. Experimental settings. Table 3. Experimental settings. ItemsContentsTarget spaceOne seat in ES4 Nakamura labSubject informationOne person (male, 20 s)Video total length700 frameVideo resolution640 × 480Frame time interval30 fpsShooting methodIntel RealSense Depth Camera D435 [25]Number of head directions5Head directionsFront,

The software service for omnichannel biometric face analysis User authentication can be as simple as taking a selfie. Secured by liveness detection, face recognition is typically used to verify if a user is who they claim to be.Our face recognition comes as a service via API and is device-independent. This first-class software enables a wide range of applications that can be used on many devices without the need to register or enroll the user on each device. How does the BioID Face Recognition Software work?","tablet":"How does Face Recognition work?","phone":"How does Face Recognition work?"}},"slug":"et_pb_text"}" data-et-multi-view-load-tablet-hidden="true" data-et-multi-view-load-phone-hidden="true">How does the BioID Face Recognition Software work? Face recognition works as a 1:1 face-matching software that verifies a selfie taken by a user against a pre-enrolled template.\nTwo selfies are required to perform liveness detection. Learn more about this in the video below:","tablet":"Face recognition is a 1:1 face-matching software that verifies a selfie taken by a user against a pre-enrolled template.\nTwo selfies are required to perform liveness detection.","phone":"Face recognition is a 1:1 face-matching software that verifies a selfie taken by a user against a pre-enrolled template.\nTwo selfies are required to perform liveness detection."}},"slug":"et_pb_text"}" data-et-multi-view-load-tablet-hidden="true" data-et-multi-view-load-phone-hidden="true">Face recognition works as a 1:1 face-matching software that verifies a selfie taken by a user against a pre-enrolled template.Two selfies are required to perform liveness detection. Learn more about this in the video below: Two Steps to Face Recognition: Enroll & Verify Enrollment Before facial verification can be conducted, the system needs to learn how you look. For enrolling, simply take a few selfies of yourself. Our proprietary AI technology transforms the characteristics of your face into an irreversible and encrypted template – a mathematical abstraction that encodes how you look. For successful follow-on face verification, selfies provided by the user must match the corresponding template kept on our BioID Web Service. Verification Once enrolled, the user can then be recognized using face verification. Face verification works the same way as enrollment except the selfies will be compared with the enrolled template. Only when there is a match, the verification process is considered successful. Secured by Liveness Detection To prevent spoofing with replay attacks (with photos, masks, video deepfakes, etc.) and to assure user presence, a liveness check is performed as part of the authentication process. Benefits of User Authentication with our Face Recognition Software Device-Independent Supports standard webcams and mobile phone cameras Platform-Independent Accessible from any online or mobile apps