System Requirements

• PC-specific:
  • x86-64 (64-bit) processors are required.
  • AVX2 support is highly recommended. Processors that do not support AVX2 will still run the MegaMatcher algorithms, but in a mode, which will not provide the specified performance. Most modern processors support this instruction set, but please check if a particular processor model supports it.
  • The CPU plugin supports inference on Intel Xeon with Intel AVX2 and AVX-512, Intel Core processors with Intel AVX2, Intel Atom Processors with Intel SSE.
  • 0.6 seconds are required to create a template with a single fingerprint, face, iris or voiceprint record using Intel Core i7-8700K processor running at 3.7 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.
• Mac-specific:
  • x86-64 (Intel) and ARM (Apple M1 family) processor architectures supported.
  • 0.6 seconds are required to create a template with a single fingerprint, face, iris or voiceprint record using Intel Core i7-8700K processor running at 3.7 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.
• 2 GB of free RAM is recommended for general usage scenarios. It is possible to reduce RAM usage for particular scenarios.
• Optionally, depending on biometric modalities and requirements:
  • A fingerprint scanner. MegaMatcher SDK includes support modules for more than 120 models of fingerprint scanners under Microsoft Windows, Linux and macOS 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 should support RTSP and stream video in H.264 or M-JPEG. Cameras, which can operate in near-infrared spectrum, can be also used for image capture. Any other webcam or camera should provide DirectShow, Windows Media or Media Foundation interfaces for Windows platform, GStreamer interface for Linux and Mac platforms.
  • 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. MegaMatcher SDK includes a programming sample, which shows how to use a flatbed scanner on Microsoft Windows platform.
  • A signature pad. The captured results are provided as vector or bitmap image, depending on supported formats. MegaMatcher SDK includes support modules for these signature pad models:
    • Dermalog LF10
    • SignoTec Sigma
    • Wacom STU-300, STU-430, STU-540
  • 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.
• Linux specific requirements:
  • Linux 4.9 or newer kernel (64-bit) is required.
  • glibc 2.24 or newer
  • GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video.
  • libgudev-1.0 230 or newer (for camera and/or microphone usage)
  • alsa-lib 1.1.6 or newer (for voice capture)
  • gcc 6.3 or newer (for application development)
  • GNU Make 3.81 or newer (for application development)
  • Java SE JDK 8 or newer (for application development with Java)
  • Python 3.x (for application development with Python)
• Microsoft Windows specific requirements:
  • Microsoft Windows 7 / 8 / 10 / 11.
  • Microsoft .NET framework 4.5 (for .NET components usage)
  • Microsoft Visual Studio 2012 or newer (for application development with C++ / C# / VB .NET)
  • Java SE JDK 8 or newer (for application development with Java)
  • Python 3.x (for application development with Python)
• macOS specific requirements:
  • macOS (version 10.13 or newer)
  • XCode 9.3 or newer (for application development)
  • GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video.
  • GNU Make 3.81 or newer (to build samples and tutorials development)
  • Java SE JDK 8 or newer (for application development with Java)

• A smartphone or tablet that is running Android 5.0 (API level 21) OS or newer.
  • 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 1 GB 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. See the technical specifications for the templates sizes with specific biometric modalities.
• 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:
    • MegaMatcher SDK includes support modules for several iris cameras under Android OS.
    • MegaMatcher technology also accepts irises for further processing as BMP, JPG, PNG or WebP 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 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 8 (or higher)
  • AndroidStudio 4.0 IDE
  • AndroidSDK 21+ API level
  • Gradle 6.8.2 build automation system or newer
  • Android Gradle Plugin 4.1.2
  • Internet connection for activating MegaMatcher component licenses

• One of the following devices, running iOS 11.0 or newer:
  • iPhone 5S or newer iPhone.
  • iPad Air or newer iPad models.
• At least 1 GB 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. See the technical specifications for the templates sizes with specific biometric modalities.
• 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, PNG or WebP 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 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 macOS 10.13 or newer.
  • Xcode 9.3 or newer.

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 Linux ARM 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 1 GB 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. See the technical specifications for the templates sizes with specific biometric modalities.
• 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, PNG or WebP formats can be processed by the MegaMatcher technology.
• glibc 2.17 or newer.
• GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good is required for face capture using camera/webcam or rtsp video.
• alsa-lib 1.1.6 or newer (for voice capture)
• libgudev-1.0 219 or newer (for camera and/or microphone usage)
• Network/LAN connection (TCP/IP) for client/server applications. Also, network connection is required for using the Matching Server component.
• Development environment specific requirements:
  • gcc 4.8 or newer
  • GNU Make 3.81 or newer
  • Java SE JDK 8 or newer

• Server hardware with at least these processors (see the technical specifications for more details):
  • Dual Intel Xeon Gold 6416H (2.2 GHz) processors for extracting a template from single fingerprint, face or palmprint images in the specified time;
  • Single Intel Xeon Gold 6416H (2.2 GHz) processor for extracting templates from single iris image, or voice samples in the specified time.
  The processors should support AVX2.
• at least 8 GB of free RAM should be available for the high-volume server application.
• Network/LAN connection (TCP/IP) for communication with client-side applications, Matching Server or MegaMatcher Accelerator unit(s).
• Linux specific requirements:
  • Linux 3.10 or newer kernel is required.
  • glibc 2.17 or newer
  • GStreamer 1.10.x or newer with gst-plugin-base and gst-plugin-good (for face capture using rtsp video)
• Microsoft Windows specific requirements:
  • Microsoft Windows Server 2008 / Server 2012 / Server 2016 / Server 2019, 64-bit.
  • Microsoft .NET framework 4.5 (for .NET components usage)

• PC, Mac or server with x86-64 (64-bit) compatible CPU.
  • Intel Core i7-8700K (3.7 GHz) processor or better is recommended.
  • AVX2 support is highly recommended. Processors that do not support AVX2 will still run the MegaMatcher algorithms, but in a mode, which will not provide the specified performance. Most modern processors support this instruction set, but please check if a particular processor model supports it.
• Enough free RAM for Matching Server code, matching engines and templates. See the technical specifications for the templates sizes with specific biometric modalities.
• 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.
• Linux specific requirements:
  • Linux 3.10 or newer kernel is required.
  • glibc 2.17 or newer
• Microsoft Windows specific requirements:
  • Microsoft Windows 7 / 8 / 10 / 11 / Server 2008 / Server 2012 / Server 2016 / Server 2019.
• macOS specific requirements:
  • macOS (OS X 10.9 or newer macOS version)