Large-scale AFIS and Multi-biometric Identification

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Large-scale AFIS and Multi-biometric Identification

Eye iris Recognition
Voice Recognition

A multi-biometric identification system combines two or more biometric methods, ensuring 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.
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Description

Operating principle

The identification procedure is performed in two steps:

» Biometric information should be captured at first (e.g. digitization of a fingerprint, face and/or eye iris) by a simple or composite receiving device/ sensor.

» Then the identification is achieved by using a combined algorithm (mathematical equation), which reaches a single result, based on the specific biometric information.

Applications

» Access Control
» Software security – User identification
» Border Control – issuance of biometric passports
» Personnel time and attendance control
» etc.

 

MegaMatcher SDK is designed for development of large-scale AFIS or multi-biometric identification products. Fingerprint, face, iris, voice and palm print recognition engines are included in MegaMatcher 13.0 SDK.

MegaMatcher 13.0 SDK 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 13.0 supports BioAPI 2.0. To ensure system compatibility with other software, WSQ component is available, as well as modules for conversion between MegaMatcher template and biometric standards.

MegaMatcher 13.0 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.

These types of MegaMatcher 13.0 SDK are available:

  • MegaMatcher 13.0 Standard SDK for developing a client/server based multi-biometric fingerprint-face-iris identification product. This SDK is suitable for network-based and web-based systems with database size ranging from several thousand records up to million records. The SDK includes ready-to-use server-side software and a set of components for developing client-side applications on Microsoft Windows, Android, iOS, Linux and Mac OS X.
  • MegaMatcher 13.0 Extended SDK for developing a large-scale network-based AFIS or multi-biometric identification product. The SDK includes all components of MegaMatcher 13.0 Standard SDK and MegaMatcher Accelerator software, which can be used for fault-tolerant scalable cluster software for fast parallel matching, processing high number of identification requests and handling databases with practically unlimited size. This SDK also allows to develop network-based and web-based systems.

There are specific requirements for running specific components on particular platforms.

System Requirments 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 i7-4771 processor running at 3.5 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 512 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. Cameras, which can operate in near-infrared spectrum, can be also used for image capture. 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)
    • 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 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)
    • 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 Requirments for MegaMatcher Client-side Components for Android

  • A smartphone or tablet that is running Android 4.4 (API level 19) 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 256 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 Requirments for MegaMatcher Client-side Components for iOS

  • One of the following devices, running iOS 8.0 or newer:
    • iPhone 5S or newer iPhone.
    • iPad 2 or newer iPad, including iPad Mini and iPad Air models.
    • iPod Touch 6th Generation or newer iPod.
  • At least 256 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 Requirments 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 256 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

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