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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 

Screenshots View All

Screenshots View All

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 

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