The MegaMatcher Standard SDK is for developing a client/server based multi-biometric face-fingerprint identification product. This SDK is suitable for network-based and web-based systems with database size ranging from several thousands records up to million records. The SDK includes ready-to-use server-side software and a set of components for developing client-side applications.

 

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Number pieces in packaging: 1
Number pieces in box: 1

 

Availabilty: Available

 

MegaMatcher Algorithm Features and Capabilities

 

MegaMatcher includes facial and fingerprint recognition engines along with a new fused algorithm for fast and reliable identification in large-scale systems. Face or fingerprint identification algorithms can be used alone to develop an automated facial identification system or an AFIS respectively. Both biometric software engines contain many proprietary algorithmic solutions that are especially useful for large-scale identification problems. These solutions were specially developed for MegaMatcher and some were inherited from the VeriFinger and VeriLook algorithms. Some of these solutions are listed below for each biometric identification engine.

 

 

MegaMatcher fingerprint identification engine

  • Rolled and flat fingerprints matching. The MegaMatcher fingerprint engine matches rolled and flat fingerprints between themselves. Usually conventional "flat" fingerprint identification algorithms perform matching between flat and rolled fingerprints less reliably due to the specific deformations of rolled fingerprints. MegaMatcher allows matching of flat-flat, flat-rolled or rolled-rolled fingerprints with high reliability. The algorithm matches up to 40,000 fingerprint records per second (on one core of Intel Core 2 Duo running at 2.66 GHz).
  • MegaMatcher includes fingerprint image quality determination, which can be used during enrollment to ensure that only the best quality fingerprint template will be stored into database.
  • Template generalization is used to generate a better quality template from several fingerprints. Better quality templates result in higher identification quality.
  • MegaMatcher is tolerant to fingerprint translation, rotation and deformation. It uses a proprietary fingerprint matching algorithm that identifies fingerprints even if they are rotated, translated and have deformations.
  • Faster matching using pre-sorted database entries. For some identification tasks MegaMatcher's fingerprint engine effective matching speed can be increased to up to 240,000 fingerprints per second by pre-sorting database entries using certain global features. Fingerprint matching is performed first with the database entries having global features most similar to those of the test fingerprint. If matching within this group yields no positive result, then the next record with the most similar global features is selected, and so on until the matching is successful or the end of the database is reached. In most cases there is a fairly good chance that the correct match will be found at the beginning of the search. As a result, the number of comparisons required to achieve fingerprint identification decreases drastically, and the effective matching speed increases correspondingly.
  • Adaptive image filtration algorithm eliminates noises, ridge ruptures and stuck ridges, and extract minutiae reliably even from poor quality fingerprints, with processing time of less than 1 second (all times are given for one core of Intel Core 2 Duo running at 2.6GHz).

 

 

MegaMatcher facial identification engine

  • Template generalization is used to generate a better quality template from several face images. Better quality templates result in higher identification quality.
  • MegaMatcher has certain tolerance to face posture that assures face enrollment convenience: rotation of a head can be up to 10 degrees from frontal in each direction (nodded up/down, rotated left/right, tilted left/right).
  • Reliable face detection assures convenient face enrollment from cameras, webcams and especially various scanned documents: faces will be found on scanned pages from passports, files etc. Multiple faces can be also detected on an image and simultaneously processed.
  • Live face detection. A conventional face identification system can be easily cheated by placing a photo of another person in front of a camera. MegaMatcher is able to prevent this kind of security breach by determining whether a face in a video stream belongs to a real human or is a photo.
  • The biometric template record can contain several face samples belonging to the same person. These samples can be enrolled from different sources and in different time, thus allowing improvement in matching quality. For example a person could be enrolled with and without eyeglasses or with different eyeglasses, with and without beard or moustache, etc.

System Requirements and Supported Development Environments

System Requirements for MegaMatcher Server and Cluster software

  • PC with x86 compatible CPU (32bit and 64bit processors are supported, Pentium4 2GHz processor or better is recommended)
  • TCP/IP network support
  • Linux specific requirements:
    • Linux 2.6 or newer
    • GCC-4.0.x or newer
    • pkg-config-0.21 or newer
    • GNU Make 3.81 or newer
    • MySQL or Oracle server (Oracle for x86-64 and any other DB servers require a custom support module to be developed by the integrator)
    • GTK+ 2.10.x or newer libs and dev packages
    • libtiff-3.8.x or newer libs and dev packages
  • Microsoft Windows specific requirements:
    • Microsoft Windows 2000/XP/2003/Vista (32bit or 64bit versions)
    • Microsoft SQL Server, MySQL or Oracle server (Oracle for x86-64 and any other DB servers require a custom support module to be developed by the integrator)

 

System Requirements for MegaMatcher Client Components:

  • PC with x86 compatible CPU (32bit and 64bit processors are supported, Pentium4 2GHz processor or better is recommended)
  • TCP/IP network support
  • Linux specific requirements:
    • Linux 2.6 or newer
    • GCC-4.0.x or newer
    • GNU Make 3.81 or newer
  • Microsoft Windows specific requirements:
    • Microsoft Windows 2000/XP/2003/Vista (32bit or 64bit versions)
    • Microsoft .NET framework 2.0 (for .NET components)
    • Microsoft Visual Studio 2005 SP1 or newer (for application development)
    • Microsoft Visual C++ 2005 SP1 runtime (for running a developed application)

 

 

Supported Development Environments

These development environments are supported by MegaMatcher SDK:

  • Microsoft Visual Studio 2005 SP1 (or newer) for Microsoft Windows platform
  • GNU C compiler for Linux platform

Reliability Testing Results and Technical Specifications

As MegaMatcher uses fusion of facial and fingerprint recognition results, the identification reliability is very high even when using large databases with millions of records. Receiver operating characteristic (ROC) chart show the reliability results for MegaMatcher 3.0.

The chart compares MegaMatcher 3.0 face matching engine reliability (blue curve), fingerprint matching engine (green curve) and the fused face-fingerprint algorithm (red curve). These ROCs show that a large-scale automated biometric identification system based on MegaMatcher provides high identification reliability when using fingerprints, and using multi-biometric identification results in a significant reliability increase, allowing the system to reach almost 0% FRR.

The fingerprints database for this test was gathered using Cross Match Verifier 300 fingerprint scanner.

These parameters were determined for one core of Intel Core 2 Duo running at 2.66 GHz

Fingerprint recognition engine
Recommended minimal fingerprint resolution 500 dpi
Single fingerprint processing time 0.2 - 0.4 seconds
Matching speed up to 40,000 fingerprint records per second
multiplied by the number of cluster nodes
Facial recognition engine
Recommended minimal face image size 640 x 480 pixels
Single face processing time about 0.2 seconds
Matching speed up to 500,000 face records per second
multiplied by the number of cluster nodes
Fused face-fingerprint identification algorithm
Matching speed up to 400,000 templates* per second
multiplied by the number of cluster nodes
Size of one template in the database
(A template can contain multiple
fingerprint and face records)
300-6,000 bytes for each fingerprint record
2,284 bytes for each face record
Maximum database size Unlimited

* A template contains one fingerprint and one face record.

MegaMatcher SDK

Release Date:
July 6, 2011
Version:4.1
30 day trial
Neurotechnology offers the 30 day trial versions of its biometric SDKs for developers and integrators.

 

A single zip archive includes trial versions of these Neurotechnology products:
  • MegaMatcher 4.1 SDK
  • VeriFinger 6.3 SDK
  • VeriLook 5.0 SDK
  • VeriEye 2.3 SDK
Developers can choose which SDK to evaluate after downloading the archive. Drivers for fingerprint scanners, face and iris cameras are included in a separate file (see below). Constant Internet connection is required during SDK evaluation.


2,557 kB


366,979 kB
MegaMatcher, VeriFinger, VeriLook, VeriEye and VeriSpeak SDK trial

Release Date:
January 13, 2012
Version:-
30 day trial
Neurotechnology offers the 30 day trial versions of its biometric SDKs for developers and integrators.

 

A single zip archive includes trial versions of these Neurotechnology products:
  • MegaMatcher 4.2 SDK
  • VeriFinger 6.4 SDK
  • VeriLook 5.1 SDK
  • VeriEye 2.4 SDK
Developers can choose which SDK to evaluate after downloading the archive. Drivers for fingerprint scanners, face and iris cameras are included in a separate file (see below). Constant Internet connection is required during SDK evaluation.


58.4 MB


374.8 MB
Customer's Reviews

"I have used VeriFinger extensively for the last 6 years. Which is why I recommended it to CDAC."

 

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Sales and Supports

Mail us on : indiasales@fulcrumbiometrics.co.in

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