. PC or Mac with 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 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.
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. x86 (32-bit) processors can still be used, but the algorithm will not provide the specified performance. at least 512 MB of free RAM should be available for the application.
MegaMatcher Standard and Extended SDK: The Standard SDK is intended for development of client/server-based multi-biometric fingerprint| face| voice| iris and palmprint identification products. The Extended SDK is intended for developing large-scale cluster-based AFIS or multi-biometric identification products. All MegaMatcher Extended SDK Trial download links are direct MegaMatcher Extended SDK Trial full download from publisher site or their selected mirrors. Avoid: pattern recognition oem software, old version, warez, serial, torrent, MegaMatcher Extended SDK Trial keygen, crack.
Optionally, depending on biometric modalities and requirements:. A fingerprint scanner.
MegaMatcher SDK includes support modules for more than 150 models of fingerprint scanners under, and 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. 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. 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 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. Linux specific requirements:.
Linux 2.6 or newer kernel 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).
GCC-4.4.x or newer (for application development). GNU Make 3.81 or newer (for application development). Sun Java 1.7 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 only on 32-bit OS or only from 32-bit applications.
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 1.7 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. GNU Make 3.81 or newer (to build samples and tutorials development). Sun Java 1.7 SDK or later (for application development with Java). 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, to find out if it is supported.
ARM-based 1.5 GHz processor recommended for processing a fingerprint, face, iris or voiceprint in the. 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. See the 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 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:. 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 mobile application may require network connection for activating the MegaMatcher component licenses. See the 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). build automation system or newer. Internet connection for activating MegaMatcher component licenses. 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. 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. See the 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. 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 mobile application may require network connection for activating the MegaMatcher component licenses. See the 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. We recommend to 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. See the for the templates sizes with specific biometric modalities. 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 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. Development environment specific requirements:. GCC-4.4.x or newer.
GNU Make 3.81 or newer. JDK 1.7 or later. Server hardware with at least these processors (see the for more details):. Dual Intel Xeon Gold 6126 (2.6 GHz) processors for extracting a template from single fingerprint or face images in the specified time;.
Single Intel Xeon Gold 6126 (2.6 GHz) processor for extracting templates from single iris image, or voice samples in the specified time. The processors should support. at least 2 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 2.6 or newer kernel is required. Linux 3.0 or newer kernel is recommended.
glibc 2.13 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). 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 1 million 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.
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 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 2.6 or newer kernel is required. Linux 3.0 or newer kernel is recommended. glibc 2.13 or newer.
Microsoft Windows specific requirements:. Microsoft Windows 7 / 8 / 10 / Server 2008 / Server 2008 R2 / Server 2012. Mac OS X specific requirements:. Mac OS X (version 10.7 or newer).
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