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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)

Version of the software. This also lets you use ADP as an access control system for your building or other secure areas.Because they’re both ADP products, your time and attendance data in ADP is seamlessly transferred to ADP RUN — the platform’s payroll processing service. This can help you consolidate multiple systems under one provider.PricingADP’s pricing is not publicly available. Request a demo on its website to get a quote.3. Jibble – Best free option for small businessesJibble’s facial recognition attendance system is one of the only apps on the market that’s completely free. With it, you get facial recognition, GPS tracking, and time tracking for unlimited users at no monthly cost. However, it’s worth noting that there are some significant limits to what you can do on the free plan. For example, you can only track time and attendance for one pay period at a time, and you don’t get its facial recognition spoofing prevention feature on the free plan.Jibble’s face recognition technology works using artificial intelligence to detect distinct facial features. When employees download the apps onto their phones or tablets, they start by taking multiple pictures of their faces from different angles. After that, Jibble uses face detection to determine which employee is attempting to log into the system.In addition to using facial recognition for authentication, Jibble also takes a picture of the employee and adds that picture to the employee’s time card. This gives you an additional layer of protection; if you believe employees are cheating the algorithm, you can review their pictures to validate or invalidate your suspicions.Time and attendance records are compiled into timesheets and reports in Jibble and can be accessed by administrators and managers anytime. Jibble also integrates with several popular payroll and project management tools to help you pass data across systems.PricingJibble is free for unlimited users for up to one pay period’s worth of time and attendance tracking. The free plan includes facial recognition (without spoofing prevention), GPS tracking, and up to two geofences.4. Timeero – Best for employee flexibilityTimeero is a face recognition attendance system that works almost identically to Buddy Punch. It integrates with Face ID to validate an employee’s face when clocking in and out. Like Buddy Punch, Timeero can only be used on Apple smartphones and tablets, and the app also comes with other accountability features like GPS tracking and geofencing.The big difference between the two apps in terms of their facial recognition features is that Timeero doesn’t block employees from clocking in if their face isn’t recognized. Instead, it simply sends a notification of the error to an admin. The admin can review the photo that was taken and see if the clock-in was legitimate

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

But, from the last 2-3 years, the usage of webcam has been grown significantly. Almost every laptop or note book is now featured with an inbuilt webcam which can be used for not only communicating with your friends but also integrating the face recognition log-in system with Windows or other operating systems easily and costlessly. If you do not have any inbuilt webcam with your computer (Desktop PC or older laptops) you can always assemble a webcam (is not so much costly these days) with it. Preparing Face Recognition Log-in with KeyLemon To setup the facial recognition authentication system at your computer, you need a simple software called as KeyLemon. It is small in size and easy to install.Laptops How to set up Windows Hello facial recognition on your laptop. You can log into your Window 10 laptop or tablet by looking at your webcam. Nov 30, 2017 Learn how to set up Windows Hello to unlock your PC with your fingerprint, face, or iris without a password.No complicated stuffs are needed. Here is the step-by-step guide.Install KeyLemon • Download the utility called as KeyLemon from. Choose the appropriate version of your system when downloading the tool (a MAC version of the tool is also available).• Once downloaded, run the application, choose your preferred language (I prefer English) and click the ‘Next’ button. • In the next wizard, you mast be agree with the license agreement to use this utility. So, check the box to agree with the agreement and click ‘Next’. • Now, choose the location where you want to or simply live it as it is and click ‘Next’. • In this step, you can choose the start menu folder and whether to create a shortcut or not. Once decided, click the ‘Install’ button. • Once completed, click the ‘Finish’ button.