MegaMatcher On Card SDK
Smart card multi-biometrics
MegaMatcher On Card SDK offers matching-on-card technology that stores a person's fingerprint, iris and face templates on a smart card and performs template matching in a microprocessor embedded in the card, instead of matching biometric information on a PC processor.
The match-on-card method ensures that personal biometric information does not transfer to an external computer as it would in a more basic template-on-card system.
MegaMatcher On Card SDK is developed utilizing a set of ISO/IEC standards to enable interoperability with and easy integration into existing smart card and/or biometric systems.
System Requirements and Supported Development Environments
There are specific requirements for running specific components on particular platforms.
Click on specific components to view the corresponding requirements.
System requirements for installation and usage of components on JavaCard
- JavaCard 2.2.1/2.2.2 compatible smart card
- See the technical specifications for the required amount of free persistent EEPROM and RAM
System requirements for PC-side components installation and usage
- PC or Mac with x86 (32bit) or x86-64 (64bit) compatible processors. 2 GHz or better processor is recommended.
- 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 required for MegaMatcher On Card PC side components deployment.
- Smart card reader. An ISO/IEC 7816 compliant smart card reader is required.
- Fingerprint scanner. MegaMatcher On Card 9.0 includes support modules for more than 80 fingerprint scanners and sensors under different platforms.
- Camera or webcam (optional) for face image capture. MegaMatcher On Card 9.0 supports a number of high resolution cameras. Any other camera or webcam is supported by MegaMatcher On Card if it provides DirectShow interface for Windows platform or GStreamer interface for Linux or Mac OS X platform.
- Iris camera (optional) for iris image capture. MegaMatcher On Card 9.0 includes support modules for several iris cameras.
Microsoft Windows specific requirements:
- Microsoft Windows XP / Vista / 7 / 8 / 10, 32-bit or 64-bit. 32-bit platform may be recommended for applications with fingerprint readers, as certain models have only 32-bit support modules.
- Microsoft .NET framework 3.5 or newer (for .NET components usage).
One of the following development environments for application development:
- Microsoft Visual Studio 2008 SP1 or newer (for application development under C/C++, C#, Visual Basic .NET)
- Sun Java 1.6 SDK or later
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 camera/webcam or rtsp video)
- libgudev-1.0 164-3 or newer (for camera usage)
- GCC-4.0.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)
- PCSC-Lite 1.4.4 or newer
- ccid-1.3.0 or newer
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 plugins (for RTSP support)
- 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 1.7.0_72 (JDK 7u72) or later (for application development with Java)
System requirements for Android components installation and usage
A smartphone or tablet that is running Android 4.0 (API level 14) OS or newer.
- API level 19 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 fast creation of fingerprint, face or iris compact template. Slower processors may be also used, but the processing of fingerprints, faces and irises will take longer time.
- At least 30 MB of free RAM should be available for the application.
Free storage space (built-in flash or external memory card):
- 30 MB required for MegaMatcher Android components deployment for each separate application.
- Additional space may be required if an integrator would like to store original fingerprint, face or iris images. MegaMatcher On Card technology does not require the original fingerprint, face or iris images to be stored after capture.
Optionally, depending on biometric modalities and requirements:
- A fingerprint reader. MegaMatcher On Card 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 On Card 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 On Card template extraction algorithm. Integrators may also use image files or receive image data from external devices like flatbed scanners or stand-alone cameras.
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 On Card SDK under Android OS.
- MegaMatcher On Card 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.
- PC-side development environment requirements: