MegaMatcher SDK

Large-scale AFIS and multi-biometric identification

MegaMatcher is designed for large-scale AFIS and multi-biometric systems developers. The technology ensures high reliability and speed of biometric identification even when using large databases.

Available as a software development kit that allows development of large-scale single- or multi-biometric fingerprint, iris, face, voice or palm print identification products for Microsoft Windows, Linux, Mac OS X, iOS and Android platforms.

System Requirements

There are specific requirements for running specific components on particular platforms.
Click on specific components to view the corresponding requirements.

System requirements for MegaMatcher client-side components for PC or Mac

  • PC or Mac with x86 (32-bit) or x86-64 (64-bit) compatible processors.
    • 0.6 seconds are required to create a template with a single fingerprint, face, iris or voiceprint record using Intel Core 2 Q9400 processor running at 2.67 GHz. See the technical specifications for more details.
    • 4 seconds are required to create a template from a full palm print image on Intel Core i7-4771 processor running at 3.5 GHz.
    • SSE2 support is required. Processors that do not support SSE2 cannot run the MegaMatcher algorithm. Please check if a particular processor model supports SSE2 instruction set.
  • at least 128 MB of free RAM should be available for the application.
  • Free space on hard disk drive (HDD):
    • at least 1 GB required for the development.
    • 100 MB for client-side components deployment.
    • Additional space optionally would be required in these cases:
      • MegaMatcher does not require the original biometric data (such as fingerprint image or photo) to be stored for the matching; it is enough to use the templates. However, we would recommend to store this data on hard drive for the potential future usage.
      • Usually a database engine runs on back-end servers (on separate computer). However, DB engine can be installed together with MegaMatcher client-side components and Matching Server on the same computer for standalone applications. In this case more HDD space for biometric templates storage must be available. For example, 1 million users templates (each with 2 fingerprint records) stored using a relational database would require from 2 GB to 12 GB of free HDD space depending on configured template size.
  • Optionally, depending on biometric modalities and requirements:
    • A fingerprint scanner. MegaMatcher SDK includes support modules for more than 100 models of fingerprint scanners under Microsoft Windows, Linux and Mac OS X platforms.
    • A webcam or IP camera or any other camera (recommended frame size: 640 x 480 pixels) for face images capturing. MegaMatcher SDK includes support modules for a list of cameras. An IP camera shold support RTSP and stream video in H.264 or M-JPEG. Any other webcam or camera should provide DirectShow interface for Windows platform, GStreamer interface for Linux platform or QuickTime interface for Mac platform.
    • An iris camera (recommended image size: 640 x 480 pixels) for iris image capture. MegaMatcher SDK includes support modules for several iris cameras.
    • A microphone. Any microphone that is supported by the operating system can be used.
    • A palm print scanner.
    • A flatbed scanner for fingerprint or palm print data capturing from paper can be used. 500 ppi or 1000 ppi FBI certified scanners are recommended. Flatbed scanners are supported only under Microsoft Windows platform and should have TWAIN drivers.
    • Integrators can also write plug-ins to support their biometric capture devices using the plug-in framework provided with the Device Manager from the MegaMatcher SDK.
  • Network/LAN connection (TCP/IP) for communication with Matching Server or MegaMatcher Accelerator unit(s). MegaMatcher client-side components can be used without network if they are used only for data collection.
    Communication is not encrypted, therefore, if communication must be secured, we would recommend to use a dedicated network (not accessible outside the system) or a secured network (such as VPN; VPN must be configured using operating system or third party tools).
  • Linux specific requirements:
    • Linux 2.6 or newer kernel (32-bit or 64-bit) is required. Linux 3.0 or newer kernel is recommended. If a fingerprint scanner is required, note that some scanners have only 32-bit support modules and will work only from 32-bit applications.
    • glibc 2.13 or newer
    • GStreamer 1.2.2 or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video. GStreamer 1.4.x or newer is recommended.
    • libgudev-1.0 164-3 or newer (for camera and/or microphone usage)
    • libasound 1.0.x or newer (for voice capture)
    • wxWidgets 3.0.0 or newer libs and dev packages (to build and run SDK samples and applications based on them)
    • Qt 4.8 or newer libs, dev and qmake packages (to build and run SDK samples and applications based on them)
    • GCC-4.4.x or newer (for application development)
    • GNU Make 3.81 or newer (for application development)
    • Sun Java 1.6 SDK or later (for application development with Java)
    • pkg-config-0.21 or newer (optional; only for Matching Server database support modules compilation)
  • Microsoft Windows specific requirements:
    • Microsoft Windows Vista / 7 / 8 / 10, 32-bit or 64-bit.
      • Note that some fingerprint scanners are supported only on 32-bit OS or only from 32-bit applications.
      • Windows XP is no longer supported in this version of the SDK. If your product requires to support Windows XP, you may consider the previous version of the SDK. Please contact us for more information.
    • Microsoft .NET framework 4.5 (for .NET components usage)
    • Microsoft Visual Studio 2012 or newer (for application development with C++ / C# / VB .NET)
    • Microsoft DirectX 9.0 or later (for face capture using camera/webcam)
    • Sun Java 1.6 SDK or later (for application development with Java)
  • Mac OS X specific requirements:
    • Mac OS X (version 10.7 or newer)
    • XCode 4.3 or newer (for application development)
    • GStreamer 1.2.2 or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video. GStreamer 1.4.x or newer is recommended.
    • wxWidgets 3.0.0 or newer libs and dev packages (to build and run SDK samples and applications based on them)
    • Qt 4.8 or newer libs, dev and qmake packages (to build and run SDK samples and applications based on them)
    • GNU Make 3.81 or newer (to build samples and tutorials development)
    • Sun Java 1.6 SDK or later (for application development with Java)

System requirements for MegaMatcher client-side components for Android

  • A smartphone or tablet that is running Android 4.4 (API level 19) OS or newer.
    • API level 22 is the recommended target for code compilation.
    • If you have a custom Android-based device or development board, contact us to find out if it is supported.
  • ARM-based 1.5 GHz processor recommended for processing a fingerprint, face, iris or voiceprint in the specified time. Slower processors may be also used, but the processing of fingerprints, faces, irises and voiceprints will take longer time.
  • At least 30 MB of free RAM should be available for the application. Additional RAM is required for applications that perform 1-to-many identification, as all biometric templates need to be stored in RAM for matching. For example, 1,000 templates (each containing 1 fingerprint and 1 face record) require about 6 MB of additional RAM. See the technical specifications for the templates sizes with specific biometric modalities.
  • Free storage space (built-in flash or external memory card):
    • 30 MB required for MegaMatcher Android components deployment for each separate application.
    • Additional space will be required if an application uses Embeddded Fast Fingerprint, Face or Iris Matcher components, as they can use flash memory instead of RAM during template matching.
    • Additional space would be required if an application needs to store original fingerprint, face or iris images, or audio samples. MegaMatcher does not require the original fingerprint, face or iris images, or audio samples to be stored for the matching; only the templates need to be stored.
  • Optionally, depending on biometric modalities and requirements:
    • A fingerprint reader. MegaMatcher is able to work with several supported fingerprint readers under Android OS. Integrators may also use image files or receive image data from external devices like flatbed scanners or other stand-alone cameras.
    • A camera for face capture. MegaMatcher is able to work with all cameras that are supported by Android OS. At least 0.3 MegaPixel (640 x 480 pixels) camera is required for the MegaMatcher biometric algorithm. Integrators may also use image files or receive image data from external devices like flatbed scanners or stand-alone cameras.
    • A microphone. MegaMatcher is able to work with all microphones that are supported by Android OS. Integrators may also use audio files or receive audio data from external devices.
    • An iris scanner. A project may require to capture iris images using some hand-held devices:
      • Iritech IriShield single iris camera is supported by the MegaMatcher SDK under Android OS.
      • MegaMatcher technology also accepts irises for further processing as BMP, JPG or PNG images, thus almost any third-party iris capturing hardware can be used with the MegaMatcher technology if it generates image in the mentioned formats.
      • Integrators may implement the iris scanner support by themselves or use the software provided by the scanners manufacturers. The integrators should note, that the most accurate iris recognition is achievable only when iris images are captured with near-infrared cameras and appropriate illumination. However, it is still possible to recognize irises with reasonable accuracy, when the irises are captured with cameras, which are built in smartphones or tablets, using proper illumination and focus, and choosing proper environment.
  • Network connection. A MegaMatcher-based embedded or mobile application may require network connection for activating the MegaMatcher component licenses. See the list of available activation options in the licensing model for more information. Also, network connection may be required for client/server applications.
  • PC-side development environment requirements:
    • Java SE JDK 6 (or higher)
    • Eclipse Indigo (3.7) IDE
    • Android development environment (at least API level 19 required)
    • One of the following build automation systems:
    • Internet connection for activating MegaMatcher component licenses

System requirements for MegaMatcher client-side components for iOS

  • One of the following devices, running iOS 8.0 or newer:
    • iPhone 5 or newer iPhone.
    • iPad 2 or newer iPad, including iPad Mini and iPad Air models.
    • iPod Touch 6th Generation or newer iPod.
  • At least 30 MB of free RAM should be available for the application. Additional RAM is required for applications that perform 1-to-many identification, as all biometric templates need to be stored in RAM for matching. For example, 1,000 templates (each containing 1 fingerprint and 1 face record) require about 6 MB of additional RAM. See the technical specifications for the templates sizes with specific biometric modalities.
  • Free storage space (built-in flash or external memory card):
    • 30 MB required for MegaMatcher iOS components deployment for each separate application.
    • Additional space would be required if an application needs to store original fingerprint, face or iris images, or audio samples. MegaMatcher does not require the original fingerprint, face or iris images, or audio samples to be stored for the matching; only the templates need to be stored.
  • Optionally, depending on biometric modalities and requirements:
    • A fingerprint reader. MegaMatcher is able to work with several supported fingerprint readers under iOS. Integrators may also use image files or receive image data from external devices like flatbed scanners or other stand-alone cameras.
    • A camera for face capture. MegaMatcher captures face images from the built-in cameras.
    • A microphone. Any smartphone's or tablet's built-in or headset microphone which is supported by iOS. Integrators may also use audio files or receive audio data from external devices.
    • An iris scanner. At the moment iris scanner support on iOS platform should be implemented by integrators. The integrators should note, that the most accurate iris recognition is achievable only when iris images are captured with near-infrared cameras and appropriate illumination. However, it is still possible to recognize irises with reasonable accuracy, when the irises are captured with cameras, which are built in smartphones or tablets, using proper illumination and focus, and choosing proper environment.
    • MegaMatcher technology also accepts fingerprint, face and iris images for further processing as BMP, JPG or PNG files, thus almost any third-party biometric capturing hardware can be used with the MegaMatcher technology if it generates images in the mentioned formats.
  • Network connection. A MegaMatcher-based embedded or mobile application may require network connection for activating the MegaMatcher component licenses. See the list of available activation options in the licensing model for more information. Also, network connection may be required for client/server applications.
  • Development environment requirements:
    • a Mac running Mac OS X 10.10.x or newer.
    • Xcode 6.4 or newer.

System requirements for MegaMatcher client-side components for ARM Linux

We recommend to contact us and report the specifications of a target device to find out if it will be suitable for running MegaMatcher-based applications.

There is a list of common requirements for ARM Linux platform:

  • A device with ARM-based processor, running Linux 3.2 kernel or newer.
  • ARM-based 1.5 GHz processor recommended for fingerprint processing in the specified time.
    • ARMHF architecture (EABI 32-bit hard-float ARMv7) is required.
    • Lower clock-rate processors may be also used, but the fingerprint, face, iris or voiceprint processing will take longer time.
  • At least 128 MB of free RAM should be available for the application. Additional RAM is required for applications that perform 1-to-many identification, as all biometric templates need to be stored in RAM for matching. For example, 1,000 templates (each containing 2 fingerprint records) require about 2 MB of additional RAM.
  • Free storage space (built-in flash or external memory card):
    • 30 MB required for MegaMatcher ARM Linux components deployment for each separate application.
    • Additional space would be required if an application needs to store original fingerprint, face or iris images, or audio samples. MegaMatcher does not require the original fingerprint, face or iris images, or audio samples to be stored for the matching; only the templates need to be stored.
  • Optionally, depending on biometric modalities and requirements:
    • A fingerprint scanner. MegaMatcher is able to work with several supported fingerprint readers under ARM Linux OS. Integrators may also use image files or receive image data from external devices like flatbed scanners or other stand-alone cameras.
    • A camera for face capture. At least 0.3 MegaPixel (640 x 480 pixels) camera is required for the MegaMatcher biometric algorithm. These cameras are supported by MegaMatcher on ARM Linux platform:
      • Any camera which is accessible using GStreamer interface.
      • Any IP camera, that supports RTSP (Real Time Streaming Protocol):
        • Only RTP over UDP is supported.
        • H.264/MPEG-4 AVC or Motion JPEG should be used for encoding the video stream.
    • An iris scanner. At the moment iris scanner support on ARM Linux platform should be implemented by integrators. The integrators should note, that the most accurate iris recognition is achievable only when iris images are captured with near-infrared cameras and appropriate illumination. However, it is still possible to recognize irises with reasonable accuracy, when the irises are captured with regular cameras, using proper illumination and focus, and choosing proper environment.
    • A microphone. Any microphone that is supported by the operating system can be used.
    • Fingerprint, face or iris images in BMP, JPG or PNG formats can be processed by the MegaMatcher technology.
  • glibc 2.13 or newer.
  • libstdc++-v3 4.7.2 or newer.
  • GStreamer 1.2.2 or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video. GStreamer 1.4.x or newer is recommended.
  • libasound 1.0.x or newer (for voice capture)
  • libgudev-1.0 164-3 or newer (for microphone usage)
  • Network/LAN connection (TCP/IP) for client/server applications. Also, network connection is required for using the Matching Server component. Communication with Matching server is not encrypted, therefore, if communication must be secured, a dedicated network (not accessible outside the system) or a secured network (such as VPN; VPN must be configured using operating system or third party tools) is recommended.
  • Development environment specific requirements:
    • GCC-4.4.x or newer
    • GNU Make 3.81 or newer
    • JDK 1.6 or later

System requirements for server-side fast template extraction components

  • Server hardware with at least these processors (see the technical specifications for more details):
    • Dual Intel Xeon E5-2680V2 (2.8 GHz) processors for extracting a template from a single fingerprint image in the specified time;
    • Single Intel Xeon E5-2680V2 (2.8 GHz) processor for extracting templates from single face or iris images, or voice samples in the specified time.
    The processors shouls support SSE2. Processors that do not support SSE2 cannot run the MegaMatcher algorithm. Please check if a particular processor model supports SSE2 instruction set.
  • at least 2 GB of free RAM should be available for the high-volume server application.
  • Free space on hard disk drive (HDD):
    • at least 1 GB required for the development.
    • 100 MB for the server-side fast template extraction components deployment.
    • Additional space optionally would be required in these cases:
      • MegaMatcher does not require to store the original biometric data (such as fingerprint image or photo); it is enough to use the templates for persons' verification or identification. However, some systems may require to store this data on hard drive for the potential future usage.
      • Usually a database engine runs on back-end servers (on separate computer). However, DB engine can be installed together with MegaMatcher client-side components and Matching Server on the same computer for standalone applications. In this case more HDD space for biometric templates storage must be available. For example, 1 million users templates (each with 2 fingerprint records) stored using a relational database would require about 2 GB of free HDD space.
  • Network/LAN connection (TCP/IP) for communication with client-side applications, Matching Server or MegaMatcher Accelerator unit(s).
    MegaMatcher does not provide any tools for encrypting the communication. If communication must be secured, we recommend to use some strong enough encryption for sending the biometric images or voice samples over the internet. Also, a dedicated network (not accessible outside the system) or a secured network (such as VPN; VPN must be configured using operating system or third party tools) may be used.
  • Linux specific requirements:
    • Linux 2.6 or newer kernel (32-bit or 64-bit) is required. Linux 3.0 or newer kernel is recommended.
    • glibc 2.11.3 or newer
    • GStreamer 1.2.2 or newer with gst-plugin-base and gst-plugin-good (for face capture using rtsp video)
  • Microsoft Windows specific requirements:
    • Microsoft Windows Server 2003 / Server 2008 / Server 2008 R2 / Server 2012, 64-bit.
    • Microsoft .NET framework 4.5 (for .NET components usage)

System requirements for Matching Server

  • PC, Mac or server with x86 (32-bit) or x86-64 (64-bit) compatible CPU.
    • 64-bit platform must be used when large databases (more than 2.5 million fingerprints or more than 400,000 users with 2 fingerprints and 1 face enrolled) used and 3 GB RAM is not enough for templates storing in RAM.
    • Intel Core i7-4771 (3.5 GHz) processor or better is recommended.
    • SSE2 support is required. Processors that do not support SSE2 cannot run the MegaMatcher algorithm. Please check if a particular processor model supports SSE2 instruction set.
  • Enough free RAM for Matching Server code (about 5 MB), matching engines and templates. 1 million users templates (each with 2 fingerprint records) require from 2 GB to 12 GB of RAM depending on configured template size. At least 20% reserve recommended and some additional memory may be taken by an operating system. Therefore to hold mentioned 1 million users data, 3 GB of free RAM is recommended for the computer running Matching Server software.
  • Free space on hard disk drive (HDD):
    • 5MB required for Matching Server software.
    • A database engine itself requires HDD space for running. Please refer to HDD space requirements from the database engine providers.
    • Enough HDD space to store templates and relation data. For example, 1 million users templates (each with 2 fingerprint records) stored using a relational database would require from 2 GB to 12 GB of free HDD space depending on configured template size. The amount of relational data depends on configuration; for example, additional 10 MB is enough for storing 1 mln users gender data.
  • Database engine or connection with it. Usually a DB engine required for the Matching Server is running on the same computer. MegaMatcher SDK contains support modules for:
    • Microsoft SQL Server (only for Microsoft Windows platform);
    • PostgreSQL (Microsoft Windows and Linux);
    • MySQL (Microsoft Windows and Linux);
    • Oracle (Microsoft Windows and Linux);
    • SQLite (all platforms);
    • memory DB (all platforms).
    The fastest option is memory DB but it does not support relational queries, therefore the recommended option is SQLite, as it requires less resources than other options but provides enough functionality.
  • Network/LAN connection (TCP/IP) for the communication with client-side applications. Communication is not encrypted, therefore if communication must be secured, we would recommend to use a dedicated network (not accessible outside the system) or a secured network (such as VPN; VPN must be configured using operating system or third party tools).
  • Linux specific requirements:
    • Linux 2.6 or newer kernel (32-bit or 64-bit) is required. Linux 3.0 or newer kernel is recommended.
    • glibc 2.11.3 or newer
  • Microsoft Windows specific requirements:
    • Microsoft Windows Vista / 7 / 8 / 10 / Server 2003 / Server 2008 / Server 2008 R2 / Server 2012, 32-bit or 64-bit.
  • Mac OS X specific requirements:
    • Mac OS X (version 10.7 or newer)
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