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Introduction

Nowadays the need for automated biometrical identification systems is increasing in civil and forensic fields of applications. The fast and accurate identification becomes particularly critical for large-scale applications, such as passport and visa documentation, border crossings, election control systems, credit card transactions control and crime scene investigations. Many countries, including the US, European countries and others incorporate biometrical data into passports, ID cards, visas and other documents for using in large national scale automatic biometrical identification systems.

Automated fingerprint identification systems (AFIS) have been widely used in forensics for the past two decades, and recently they became relevant for civil applications. Whereas large-scale biometrical applications require high identification speed and reliability, multi-biometric systems that incorporate both face and fingerprint recognition offer a number of advantages for improving identification quality and usability.

Large-scale automatic biometrical identification systems have a number of special requirements, which are different from those for small or middle scale biometrical systems:

• The system must perform reliable identification with large databases, as biometrical identification systems tend to accumulate False Acceptance Rate with database size increase and using single fingerprint or face image for identification task becomes unreliable for large-scale application. Several biometrical samples should be used to increase identification reliability, and multi-biometrical technologies (i.e. collecting fingerprint and face samples from the same person) are often employed there for additional convenience.
• The system must show high productivity and efficiency, which correspond its scale:
   ° System scalability is important, as the system might be extended in the future, so high productivity level should be kept by adding new units to the existing system.
   ° Daily number of identification requests could be very high.
   ° Identification request should be processed in a very short time (ideally – in real time), thus high computational power is required.
   ° Support for large databases (tens or hundreds millions of records) is required.
   ° General system robustness. The system must be tolerant to hardware failures, as even temporary pauses in its work may cause big problems taking into account the application size.
• The system must support major biometrical standards. This should allow using the system generated templates or databases with the systems from other vendors and vice versa.
• The system must be able to match flat (plain) fingerprints with rolled fingerprints, as many institutions collect rolled fingerprint databases.
• The system must be able to work in the network, as in most cases client workstations are remote from the server with the central database.
• A forensic system must be able to edit latent fingerprint templates in order to submit latent fingerprints into AFIS for the identification.

Despite all these requirements, the system price should be as low as possible. Many existing AFIS are specialized for criminalistics or other particular applications and are quite expensive. Neurotechnology offers a technology for large-scale AFIS and multi-biometric face-fingerprint identification products, which meets all of the requirements mentioned above, for a competitive price.

Why MegaMatcher?

Neurotechnology has experience in collaborating with many biometrical system integrators, who develop large-scale biometrical systems. To address their requirements, the company has developed the MegaMatcher multi-biometrical technology, intended for large-scale face-fingerprint systems and AFIS integrators. MegaMatcher has a set of specific features, which make it very attractive for large-scale biometric system developers:

• Multibiometrics. MegaMatcher includes fingerprint and facial recognition engines and allows integrators to use fused algorithm for better identification results or any of these engines separately. Identification reliability is a very important requirement for a large-scale system, thus usually two or more different biometrical samples from the same person are used to increase recognition reliability. Using MegaMatcher 2.1's multi-biometric technology, developers and integrators can create systems where both face and fingerprint can be scanned at the same time using inexpensive hardware, such as a fingerprint scanner and a simple webcam or photo scanner (for example, scanning a passport photo).

• Reliability. As MegaMatcher uses fusion of facial and fingerprint recognition results, the identification reliability is very high even when using large databases with millions of records. Receiver operating characteristic (ROC) curves show the reliability results for MegaMatcher 2.1. The chart compares MegaMatcher 2.1 face identification engine reliability (blue curve), fingerprint identification engine (green curve) and the fused face-fingerprint algorithm (red curve). These ROCs show that large-scale automated biometrical identification system based on MegaMatcher provides high identification reliability when using fingerprints, and using multi-biometrical identification results in significant reliability increase, allowing to reach almost 0% FRR.

• Matching speed. MegaMatcher is able to match up to 400,000 templates per second running the fused algorithm on a stand-alone PC with 3GHz CPU. MegaMatcher's facial recognition engine is able to match up to 500,000 faces per second, and the fingerprint recognition engine matches up to 60,000 fingerprints per second. The matching speed could be significantly increased by using the PC-based cluster.

• MegaMatcher includes cluster software for performing parallel matching, which allows to reach high performance, high availability and efficiency:
   ° The effective matching speed increases proportionally to the number of the cluster's nodes and can be scalable to achieve the necessary system performance. For example, a cluster-based multi-biometrical identification system with 10 nodes is able to match up to 4,000,000 records per second, a cluster with 100 nodes - up to 40 millions records per second etc. Such scalable architecture allows to keep up the fast system response if its size becomes larger.
   ° A large number of identification requests could be processed by the cluster-based multi-biometrical system. Suppose, there is a database with 10 million records. A cluster with 10 nodes (PCs with 3GHz CPU) will be able to process about 30,000 requests per day with the given database, a cluster with 20 nodes – about 60,000 requests per day and so on.
   ° Fast request processing. The scalable cluster architecture for automated biometrical identification system allows to achieve real-time processing of the identification request.
   ° The cluster is able to handle databases of a practically unlimited size.
   ° Computer cluster is fault-tolerant, so in case of a cluster node fault, the matching speed slightly decreases, but the cluster's work remains uninterrupted.

• MegaMatcher supports BioAPI 2.0 (ISO/IEC 1978-1:2006) and other biometrical standards:
   ° ISO/IEC 19794-2 (Information technology – Biometric data interchange formats – Part 2: Fingerprint minutiae data)
   ° ISO/IEC 19794-4 (Information technology – Biometric data interchange formats – Part 4: Finger image data)
   ° ISO/IEC 19794-5 (Information technology – Biometric data interchange formats – Part 5: Face image data)
   ° ANSI INCITS 378-2004 (Finger Minutiae Format for Data Interchange)(ANSI378)
   ° ANSI INCITS 381-2004 (American National Standard for Information Technology – Finger Image-Based Data Interchange Format)
   ° ANSI INCITS 385-2004 (American National Standard for Information Technology – Face Recognition Format for Data Interchange)
   ° ANSI/NIST-ITL 1-2000 (Data format interchange of Fingerprint, Facial, and Scar Mark and Tattoo (SMT) Information) (AN2K)
Therefore, MegaMatcher fingerprint templates could be exported to another identification system and vice versa. Additionally, MegaMatcher supports WSQ fingerprint image storage format.

• The technology allows to match rolled and flat fingerprints between themselves. Usually conventional "flat" fingerprint identification algorithms perform matching between flat and rolled fingerprints less reliably due to the specific deformations of rolled fingerprints. MegaMatcher allows matching of flat-flat, flat-rolled or rolled-rolled fingerprints with high reliability.

• MegaMatcher includes network support, as components of MegaMatcher are intended to be distributed on the network.

• Effective price/performance ratio. MegaMatcher uses a PC and can work with Microsoft Windows and Linux operating systems. This configuration provides the most price/performance effective computational units for all components of the system. Therefore, developing with MegaMatcher SDK means that the system price will be reasonable for both software and hardware parts.

• MegaMatcher is fully compatible with other Neurotechnology's products: VeriFinger, VeriLook, FingerCell and FaceCell.

Algorithm

MegaMatcher includes facial and fingerprint recognition engines and allows to use the new fused algorithm for fast and reliable identification in large-scale systems. Face or fingerprint identification algorithms can be used alone to develop an automated facial identification system or an AFIS respectively. Both biometrical software engines contain many proprietary algorithmic solutions, which are especially useful for large-scale identification problems. These solutions were specially developed for MegaMatcher, and some were inherited from the VeriFinger and VeriLook algorithms. Some of these solutions are listed below for each biometrical identification engine.

MegaMatcher fingerprint identification engine

• Full MINEX Certification. NIST has certified MegaMatcher fingerprint technology for use in personal identity verification program applications.
• MegaMatcher includes fingerprint image quality determination which can be used during enrollment to ensure that only the best quality fingerprint template will be stored into database.
• Template generalization is used to generate a better quality template from several fingerprints. Better quality templates result in higher identification quality.
• MegaMatcher is tolerant to fingerprint translation, rotation and deformation. It uses a proprietary fingerprint matching algorithm, which identifies fingerprints even if they are rotated, translated and have deformations.
• MegaMatcher algorithm is able to match rolled fingerprints, flat fingerprints, and also rolled with flat between themselves. Due to the specific scanning technique (rolling from nail to nail) rolled fingerprints usually have much bigger deformation than those scanned using the "flat" technique. MegaMatcher matches rolled fingerprints very well, as it is tolerant to fingerprint deformations.
• MegaMatcher can use database entries which were pre-sorted using certain global features and matches about 60,000 fingerprints per second using the pre-sorted records. Fingerprint matching is performed first with the database entries having global features most similar to those of the test fingerprint. If matching within this group yields no positive result, then the next record with the most similar global features is selected, and so on until the matching is successful or the end of the database is reached. In most cases there is a fairly good chance that the correct match will be found at the beginning of the search. As a result, the number of comparisons required to achieve fingerprint identification decreases drastically, and the effective matching speed increases correspondingly.
• Adaptive image filtration algorithm allows to eliminate noises, ridge ruptures and stuck ridges, and extract minutiae reliably even from poor quality fingerprints, with processing time of less than 1 second (all times are given for one core of Intel Core 2 Duo running at 2.6GHz).

MegaMatcher facial identification engine

• Template generalization is used to generate a better quality template from several face images. Better quality templates result in higher identification quality.
• MegaMatcher has certain tolerance to face posture that assures face enrollment convenience: rotation of a head can be up to 10 degrees from frontal in each direction (nodded up/down, rotated left/right, tilted left/right).
• Reliable face detection assures convenient face enrollment from cameras, webcams and especially various scanned documents: faces will be found on scanned pages from passports, files etc. Multiple faces can be also detected on an image and simultaneously processed.
• Live face detection. A conventional face identification system can be easily cheated by placing a photo of another person in front of a camera. MegaMatcher is able to prevent this kind of security breach by determining whether a face in a video stream belongs to a real human or is a photo.
• Biometrical template record can contain several face samples belonging to the same person. These samples can be enrolled from different sources and in different time thus allowing to improve matching quality. For example a person could be enrolled with and without eyeglasses or with different eyeglasses, with and without beard or moustache, etc.

Technical Specifications

These parameters were determined for one core of Intel Core 2 Duo running at 2.6GHz

Related Products

MegaMatcher 2.1 SDK is based on MegaMatcher algorithm. These types of MegaMatcher 2.1 SDK are available:

• MegaMatcher 2.1 Standard SDK for developing a client/server based multi-biometric face-fingerprint identification product.
• MegaMatcher 2.1 Extended SDK for developing a large-scale cluster-based AFIS or multi-biometric identification product.

30 day trial versions of MegaMatcher 2.1 Standard SDK and Extended SDK are available for downloading.

SDK Overview 

MegaMatcher is intended for development of AFIS or multi-biometric face-fingerprint identification products.

The MegaMatcher multi-biometrical technology ensures high reliability and speed of biometrical identification even when using large databases. High productivity and efficiency are supported by a fused algorithm that contains fingerprint and facial recognition engines. Integrators can use the fused algorithm for better identification results or any of these engines separately. MegaMatcher fingerprint engine has received NIST MINEX Certification.

MegaMatcher 2.1 includes server-side software and a set of modules for developing client-side applications. .NET components are included for rapid development of client-side software. MegaMatcher 2.1 supports BioAPI 2.0. To ensure system compatibility with other software, WSQ library is included, as well as modules for conversion between MegaMatcher template and biometrical standards (ISO/IEC 19794-2, ISO/IEC 19794-4, ISO/IEC 19794-5, ANSI INCITS 381-2004, ANSI INCITS 385-2004, ANSI/NIST ITL-1-2000 and ANSI/INCITS 378-2004).

MegaMatcher 2.1 is suitable not only for developing civil AFIS, but also for forensic AFIS applications, as it includes an API for latent fingerprint template editing. Latent fingerprint template editing is necessary in order to submit a latent fingerprint (for example, one taken from a crime scene) for the identification into AFIS. Also MegaMatcher is able to match rolled and flat fingerprints between themselves.

MegaMatcher 2.1 Standard SDK and Extended SDK

There are these types of MegaMatcher 2.1 SDK:

• MegaMatcher 2.1 Standard SDK (formerly known as MegaMatcher Light SDK) for developing a client/server based multi-biometric face-fingerprint identification product. This SDK is suitable for network-based and web-based systems with database size ranging from several thousands records up to million records. The SDK includes ready-to-use server-side software and a set of components for developing client-side applications.

• MegaMatcher 2.1 Extended SDK (formerly known as MegaMatcher SDK) for developing a large-scale network-based AFIS or multi-biometric identification product. The fault-tolerant scalable cluster software allows to perform fast parallel matching, processes high number of identification requests and handles databases with practically unlimited size. The SDK includes all components of MegaMatcher 2.1 Standard SDK and ready-to-use cluster server and node software. This SDK also allows to develop network-based and web-based systems.

The table below compares MegaMatcher 2.1 Standard SDK and MegaMatcher 2.1 Extended SDK.

List of components, supported scanners and platforms

The table below explains which modules can run on the specified platforms.

MegaMatcher Cluster Server

MegaMatcher 2.1 Cluster Server licenses and software are not included in MegaMatcher 2.1 Standard SDK.

Cluster is a set of software and hardware components which solves a problem of computing power deficiency. For example, there is a task to identify a person using a database with 100 millions biometrical records. A stand-alone PC with a 3GHz processor and appropriate automated biometrical identification software will need 5-60 minutes to match this person. A cluster-based system of 10 PCs will need approximately 10 times less time to do that, 100 PCs - 100 times less time and so on.

Generally, the cluster-based automated biometrical identification system takes a template with person's biometric data and searches for the person in the database of templates. There are two possible results of the search: a set of templates matched against the given template, or affirmation that the given template doesn't match any entry in the database.

MegaMatcher Cluster Server splits the templates database and distributes it between cluster nodes.

Microsoft SQL Server, Oracle and MySQL database support modules with source codes are included for Cluster Server component. Custom modules for working with other databases can also be developed by integrator and used with MegaMatcher Cluster Server components. See the table above for details on platform compatability.

MegaMatcher Cluster Node 

MegaMatcher 2.1 Cluster Node licenses and software are not included in MegaMatcher 2.1 Standard SDK.

MegaMatcher Cluster Node is a component of the cluster, which performs the actual template matching using the MegaMatcher's fused face-fingerprint identification algorithm or, optionally, face or fingerprint identification engine. Each Cluster Node performs template matching within its own part of the database. Obviously, a larger number of nodes results in faster matching, because each node operates on a smaller part of the database. Cluster Node can store templates using a database, or using RAM for achieving better performance.

Microsoft SQL Server, Microsoft Access, SQLite and MySQL support modules (with source code) are included for node component. See the table above for details on platform compatability.

MegaMatcher Server 

• MegaMatcher Server runs on single PC and is intented for moderate size systems like local AFIS or multi-biometric identification system. The Server performs the biometrical template matching and provides the same functionality as MegaMatcher Cluster software, except the parallel matching ability.

• Microsoft SQL Server, Oracle and MySQL database support modules with source codes are included for Server component. Custom modules for working with other databases can also be developed by integrator and used with MegaMatcher Server component. See the table above for details on platform compatability.

• Source code of sample web server software. The software accepts biometrical templates from MegaMatcher web client application, sends them to MegaMatcher Server for matching and returns matching results to the client application. The web server is stand-alone and does not require any third-party web server software (like Apache or Microsoft IIS).

MegaMatcher Client 

MegaMatcher Standard and Extended SDKs include a set of modules that are intended for the development of biometrical system's client-side applications. A client application gathers biometrical samples, extracts biometrical template information from them and sends extracted template for matching to MegaMatcher Server and/or MegaMatcher Cluster Server.

• MegaMatcher Extractor module performs fingerprint or facial image processing and extracts unique biometrical features, that are sent to MegaMatcher Server or Cluster Server for identification. The Extractor module can be used with fingerprint images from fingerprint scanners and/or files and with face images from cameras and/or files.
.NET wrapper for MegaMatcher Extractor module is included. The module is compiled in native code therefore .Net applications require a special wrapper to access module's API.
• The Client communication module allows sending a task to MegaMatcher Server, querying status of the task, getting the results and removing the task from server. This component hides all low level communications and provides high-level API for the developer.
• Fingerprint view component (.NET) shows captured fingerprint image. This component is also able to show extracted minutia points.
• Fingerprint segmentation module separates fingerprints if an image contains more than one fingerprint. This component allows to scan a tenprint card and enrol all fingerprints simultaneously or use scanners that allow to scan two or more fingers at once.
• Fingerprint pattern classification module allows to determine a fingerprint pattern class. The classification is usually used in forensics, but also it can be used to increase fingerprint matching speed. The defined classes are:
   ° Left Slant Loop;
   ° Right Slant Loop;
   ° Tented Arch;
   ° Whorl;
   ° Scar;
   ° "Unknown" – for the nondetermined classes.
• Scanners support component allows manipulating scanners that are connected to the PC. See the table above for the list of supported scanners and platform compatability.
• Camera support component allows manipulating cameras and webcams that are connected to the PC.
• Latent Fingerprint Editor. In most cases automated image processing is unable to extract all minutiae or extracts a lot of false minutiae from latent fingerprint image (for example, taken from the crime scene). Therefore, an expert should manually edit a fingerprint template in order to submit it to an AFIS for the identification.
Sample latent fingerprint template editor shows how to change minutia's coordinates, direction, type and other parameters. Sample editor is available for Microsoft .Net environment.

• NImages Pro (WSQ) library. WSQ (Wavelet Scalar Quantization) fingerprint image compression allows compressing image up to 10-15 times. WSQ compression process is "lossy", meaning that the reconstructed image isn't equal to the original (some information has been lost). However, the WSQ algorithm was specially designed to minimize the loss of fingerprint information, so that the reconstructed image is as close as possible to the original.
MegaMatcher 2.1 SDK contains a WSQ compression and decompression library, which can be used to minimize storage size of fingerprint images and for data interchange between systems. .NET wrapper is included.
• Biometric Standard Support modules. These modules can be used for biometric data interchange between MegaMatcher and biometrical systems. The following standards are supported:
   ° BioAPI 2.0 (ISO/IEC 1978-1:2006) Framework and Biometric Service Providers (BSP) for face and fingerprint identification engines
   ° ISO/IEC 19794-2 (Information technology – Biometric data interchange formats – Part 2: Fingerprint minutiae data)
   ° ISO/IEC 19794-4 (Information technology – Biometric data interchange formats – Part 4: Finger image data)
   ° ISO/IEC 19794-5 (Information technology – Biometric data interchange formats – Part 5: Face image data)
   ° ANSI INCITS 378-2004 (Finger Minutiae Format for Data Interchange)(ANSI378)
   ° ANSI INCITS 381-2004 (American National Standard for Information Technology – Finger Image-Based Data Interchange Format)
   ° ANSI INCITS 385-2004 (American National Standard for Information Technology – Face Recognition Format for Data Interchange)
   ° ANSI/NIST-ITL 1-2000 (Data format interchange of Fingerprint, Facial, and Scar Mark and Tattoo (SMT) Information) (AN2K)
MegaMatcher Biometric Standard Support modules can also be used to edit all standard templates, except ANSI/NIST-ITL 1-2000.
.NET wrappers for each module are included.

Supported development environments 

These development environments are supported:

• Microsoft Visual Studio 2005 SP1 (or newer) for Microsoft Windows platform
• GNU C compiler for Linux platform

System requirements

System requirements for Server and Cluster:
• PC with x86 compatible CPU (32bit and 64bit processors are supported, Pentium4 2GHz processor or better is recommended)
• TCP/IP network support
• Linux specific requirements:
   ° Linux 2.6 or newer
   ° GCC-4.0.x or newer
   ° pkg-config-0.21 or newer
   ° GNU Make 3.81 or newer
   ° MySQL or Oracle server (Oracle for x86-64 and any other DB servers require a custom support module to be developed by the integrator)
   ° GTK+ 2.10.x or newer libs and dev packages
   ° libtiff-3.8.x or newer libs and dev packages
• Microsoft Windows specific requirements:
   ° Microsoft Windows 2000/XP/2003/Vista (32bit or 64bit versions)
   ° Microsoft SQL Server, MySQL or Oracle server (Oracle for x86-64 and any other DB servers require a custom support module to be developed by the integrator)

System requirements for client components:
• PC with x86 compatible CPU (32bit and 64bit processors are supported, Pentium4 2GHz processor or better is recommended)
• TCP/IP network support
• Linux specific requirements:
   ° Linux 2.6 or newer
   ° GCC-4.0.x or newer
   ° GNU Make 3.81 or newer
• Microsoft Windows specific requirements:
   ° Microsoft Windows 2000/XP/2003/Vista (32bit or 64bit versions)
   ° Microsoft .NET framework 2.0 (for .NET components)
   ° Microsoft Visual Studio 2005 SP1 or newer (for application development)
   ° Microsoft Visual C++ 2005 SP1 runtime (for running a developed application)

SDK trials

Neurotechnology offers MegaMatcher 2.1 Standard SDK and Extended SDK on 30 day trial. The trial SDKs allow to explore SDKs' possibilities and to try them in real environment and real application. Constant Internet connection is required during evaluation.
MegaMatcher 2.1 SDK trials are available for downloading.

Licensing MegaMatcher SDK components 

To develop a product based on MegaMatcher technology, an integrator should obtain MegaMatcher 2.1 Standard SDK (EUR 2,590) or MegaMatcher 2.1 Extended SDK (EUR 4,990). Integrators can develop only an end-user product using MegaMatcher SDK and sell/install the product to their own customers*.

MegaMatcher 2.1 components are:
• MegaMatcher 2.1 Cluster Server
• MegaMatcher 2.1 Cluster Node
• MegaMatcher 2.1 Server
• MegaMatcher 2.1 Client

A license is required for each running instance of a MegaMatcher 2.1 component. The following license types are available:
• Single computer license.
• Concurrent network license
• Enterprise license.

MegaMatcher 2.1 Standard SDK already includes:
• 1 MegaMatcher 2.1 Server installation license
• 2 MegaMatcher 2.1 Client installation licenses

MegaMatcher 2.1 Extended SDK already includes:
• 1 MegaMatcher 2.1 Cluster Server installation license
• 2 MegaMatcher 2.1 Cluster Node installation licenses
• 1 MegaMatcher 2.1 Server installation license
• 2 MegaMatcher 2.1 Client installation licenses

MegaMatcher 2.1 Standard SDK and MegaMatcher 2.1 Extended SDK customers can obtain additional MegaMatcher 2.1 components licenses for their product installation or development at any time. Prices for additional MegaMatcher component licenses can be found here.
Please, also refer to MegaMatcher SDK Software License Agreement for all licensing terms and conditions.

Single computer licenses

A single computer license allows to install and run a MegaMatcher 2.1 component installation on one processor core. License will not be lost if computer will be reinstalled.

The following license management options are available:
• license activation online by communicating with Neurotechnology's server;
• license activation by email;
• license activation using volume license manager;
• license management using volume license manager on LAN or Internet.

Concurrent network licenses

A concurrent network license allows to install MegaMatcher 2.1 Client component on an unlimited number of computers. Volume license manager is used to manage these licenses across the computers on LAN or Internet. The allowed number of simultaneously running MegaMatcher 2.1 Client components equals to the number of concurrent licenses in the license manager.

This type of licensing is especially useful for web-based software.

MegaMatcher enterprise license

MegaMatcher enterprise license allows an unlimited use of MegaMatcher components (Cluster Servers, Servers, Nodes and Clients) in the end-user products in the certain territory, market segment or project. These limitations would be included in the licensing agreement.

The enterprise license price depends on the application size and the number of potential application's users within the designated territory, market segment or project. MegaMatcher enterprise licenses are provided only for big projects, with price range starting at EUR 80,000.

For more information please contact us.

Volume license manager

Volume license manager is used on site by integrators or end users to manage obtained licenses for MegaMatcher 2.1 components. It consists of license management software and a dongle, which are used to store the number of obtained licenses. An integrator or an end-user can use the volume license manager in the following ways:

• Activating the single computer licenses. An installation license for a MegaMatcher 2.1 component will be activated for using on a particular computer. The license quantity in the license manager will be decreased by the amount of activated licenses.
• Managing the single computer licenses on LAN or Internet. The license manager allows to manage installation licenses for MegaMatcher components across the computers on LAN or Internet. The number of managed licenses is limited by the number of licenses in the license manager. No license activation is needed and the license quantity is not decreased. Once issued, the license is assigned to certain computer on the network.
• Managing the concurrent licenses on LAN or Internet.
This type of licensing is especially useful for web-based software.
Each concurrent license allows to run MegaMatcher 2.1 Client component on a selected computer on a network, with an ability to move the license to another computer after finishing the work on the current one. The number of shared licenses is limited by the number of licenses in the volume license manager. No license activation is needed. Once issued, the license is assigned to certain computer on LAN or Internet. The license can be released after finishing using the MegaMatcher 2.1 Client based application and moved to another computer.
• Using a license manager as a dongle. The volume license manager containing at least one license for a MegaMatcher 2.1 component can be used as a dongle that allows to run the MegaMatcher 2.1 component on a particular computer.

Additional MegaMatcher 2.1 component licenses for the license manager can be purchased anytime. Neurotechnology will generate a special update code and send it to you. Then you will just have to enter the code to the license manager to add these purchased licenses.

* If the integrator wants to develop and sell a MegaMatcher based development tool (with API, programming possibilities, programming samples, etc.), he/she will need a permission from Neurotechnology and shall sign a special VAR agreement.

Download

• MegaMatcher 2.1 Standard SDK 30 Day Trial
• MegaMatcher 2.1 Extended SDK 30 Day Trial

Prices