OpenFaceTracker Description
OpenFaceTracker is a facial recognition application designed to recognize one or more faces in images or videos by using a database for identification. To run OpenFaceTracker, your system must have OpenCV 3.2 and QT4 installed; you can either compile the libraries manually by following build_oft or install OpenCV and QT through your preferred package manager. You have the option to compile OpenFaceTracker either as a library or as a standalone executable. Once compiled, you can open the resulting file to utilize the detection and recognition features, display help and exit options, list all available cameras, test the XML database, read the configuration settings, and verify environmental parameters. OpenFaceTrackerLib is built on OpenCV 3.2, which has brought numerous new algorithms and enhancements compared to version 2.4, with several modules being restructured and rewritten. While most algorithms from version 2.4 remain available, the interfaces may vary, necessitating users to familiarize themselves with the changes. Ultimately, OpenFaceTracker offers a versatile solution for facial recognition tasks across various platforms.
OpenFaceTracker Alternatives
For developers, ARGOS Identity offers two APIs.
ARGOS ID Check (KYC/eKYC): launch the hosted Liveform from one URL in a browser or an iOS, Android or React Native WebView, or build your own capture UI on the Verify engine APIs. Your backend uses the REST API to fetch submissions, review pending cases, delete or partially delete data for GDPR and CCPA requests and download AML reports, and receives webhooks for created, submit, approved, rejected, updated, delete and AML result events. Each check reads the ID with OCR plus MRZ and PDF417 barcode checks, tests ID and selfie liveness, matches the face to the ID photo and screens the user against sanctions and PEP lists, with ongoing monitoring afterwards. Data is processed and stored in the region chosen for each project (US or South Korea), sensitive fields are encrypted with AES-256 at rest, and payload encryption is optional. A migration API imports existing verification records.
ARGOS Omni (Review Ops Agent): authenticate with an x-api-key header, create a profile under a workflow, upload files, text or JSON as items (PDF, DOCX, XLSX, PPTX, images, CSV, HTML, EML and more) and start an asynchronous analysis. The result follows the output schema you defined, with a verdict of verified, rejected or pending_review and the reasoning and source step for each extracted value, and every completed analysis can be downloaded as a PDF report.
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Sumsub is a single verification platform that allows you to onboard more customers worldwide, speed up their access, reduce costs, and fight digital fraud. Sumsub combines effective verification flows with higher conversion rates worldwide through a powerful, all in one suite designed for a wide variety of needs: KYC/AML verification, KYB verifications, payment fraud prevention and face authentication.
Learn more about Sumsub
Darknet
Darknet is a neural network framework that is open-source, developed using C and CUDA. Known for its speed and simplicity in installation, it accommodates both CPU and GPU processing. The source code is available on GitHub, where you can also explore its capabilities further. The installation process is straightforward, requiring only two optional dependencies: OpenCV for enhanced image format support and CUDA for GPU acceleration. While Darknet performs efficiently on CPUs, it boasts a performance increase of approximately 500 times when running on a GPU! To leverage this speed, you'll need an Nvidia GPU alongside the CUDA installation. By default, Darknet utilizes stb_image.h for loading images, but for those seeking compatibility with more obscure formats like CMYK jpegs, OpenCV can be employed. Additionally, OpenCV provides the functionality to visualize images and detections in real-time without needing to save them. Darknet supports the classification of images using well-known models such as ResNet and ResNeXt, and it has become quite popular for employing recurrent neural networks in applications related to time-series data and natural language processing. Whether you're a seasoned developer or a newcomer, Darknet offers an accessible way to implement advanced neural network solutions.
Learn more about Darknet
OpenCV
OpenCV, which stands for Open Source Computer Vision Library, is a freely available software library designed for computer vision and machine learning. Its primary goal is to offer a unified framework for developing computer vision applications and to enhance the integration of machine perception in commercial products. As a BSD-licensed library, OpenCV allows companies to easily adapt and modify its code to suit their needs. It boasts over 2500 optimized algorithms encompassing a wide array of both traditional and cutting-edge techniques in computer vision and machine learning. These powerful algorithms enable functionalities such as facial detection and recognition, object identification, human action classification in videos, camera movement tracking, and monitoring of moving objects. Additionally, OpenCV supports the extraction of 3D models, creation of 3D point clouds from stereo camera input, image stitching for high-resolution scene capture, similarity searches within image databases, red-eye removal from flash photographs, and even eye movement tracking and landscape recognition, showcasing its versatility in various applications. The extensive capabilities of OpenCV make it a valuable resource for developers and researchers alike.
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Integrations
API:
Yes, OpenFaceTracker has an API
No Integrations at this time
Company Details
Company:
OpenFaceTracker
Year Founded:
2017
Website:
www.openfacetracker.net
Media
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Product Details
Platforms
Web-Based
Windows
Linux
Types of Training
Training Docs
Customer Support
Online Support
OpenFaceTracker Features and Options
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