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Description
IriCoreLite serves as an advanced iris recognition library that equips developers and system integrators with a robust array of application programming interfaces and functionalities to create applications centered around iris recognition technology. Tailored for extensive iris identification implementations on both PCs and enterprise systems, it operates seamlessly with IriTech's iris scanners. The library features precise iris segmentation techniques for feature extraction that utilize variable multi-sector analysis alongside non-linear segmentation, a sophisticated image enhancement tool to adapt to diverse lighting conditions and obstacles, and an efficient occlusion detection system designed to filter out eyelids and eyelashes. Its swift and precise matching algorithm is optimized for handling large databases, while a comprehensive image quality assessment feature guarantees dependable input data. Furthermore, the algorithms within IriCoreLite have undergone stringent evaluations during NIST testing and have consistently demonstrated their effectiveness across various public databases, showcasing their reliability and performance in real-world applications. This makes IriCoreLite a solid choice for developers seeking to implement cutting-edge iris recognition capabilities in their projects.
Description
The VLFeat open source library offers a range of well-known algorithms focused on computer vision, particularly for tasks such as image comprehension and the extraction and matching of local features. Among its various algorithms are Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, the agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, and large scale SVM training, among many others. Developed in C to ensure high performance and broad compatibility, it also has MATLAB interfaces that enhance user accessibility, complemented by thorough documentation. This library is compatible with operating systems including Windows, Mac OS X, and Linux, making it widely usable across different platforms. Additionally, MatConvNet serves as a MATLAB toolbox designed specifically for implementing Convolutional Neural Networks (CNNs) tailored for various computer vision applications. Known for its simplicity and efficiency, MatConvNet is capable of running and training cutting-edge CNNs, with numerous pre-trained models available for tasks such as image classification, segmentation, face detection, and text recognition. The combination of these tools provides a robust framework for researchers and developers in the field of computer vision.
API Access
Has API
Yes
API Access
Has API
No
Integrations
No details available.
Integrations
No details available.
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
No
Linux
Yes
Chromebook
No
Deployment
Web-Based
No
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Customer Support
Business Hours
Yes
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
Yes
Live Training (Online)
Yes
In Person
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Iritech, Inc.
Founded
2000
Country
United States
Website
iritech.com/iricorelite/
Vendor Details
Company Name
VLFeat
Country
United States
Website
www.vlfeat.org/matconvnet/
Product Features
Product Features
Deep Learning
Convolutional Neural Networks
Yes
Document Classification
Yes
Image Segmentation
Yes
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
No