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  MegaMatcher, Scalable AFIS Technology

 At a Glance  Features  Download


 



At a Glance

MegaMatcher technology is designed for large-scale AFIS and multi-biometric systems developers. The technology ensures high reliability and speed of biometric identifi cation even when using large databases. MegaMatcher is available as a software development kit that allows development of large-scale single- or multibiometric fingerprint, iris, face, voice or palm print identifi cation products for Microsoft Windows, Linux, Mac OS X and Android platforms.

  • NIST MINEX-compliant fi ngerprint engine, NIST IREX proven iris engine.
  • Includes fi ngerprint, iris, face, voice and palm print modalities.
  • Rolled, fl at and latent fi ngerprint matching.
  • BioAPI 2.0 and other ANSI and ISO biometric standards support.
  • Multiplatform, scalable cluster architecture for parallel matching.
  • Effective price/performance ratio, fl exible licensing and free customer support.

 


Advantages of MegaMatcher

  • Proven in national-scale projects, including passport issuance and voter deduplication.
  • NIST MINEX-compliant fingerprint engine,
    NIST IREX proven iris engine.
  • 200,000,000 irises or 100,000,000 fingerprints or faces per second can be matched with MegaMatcher Accelerator.
  • Fingerprints, irises and faces can be matched on smart cards using MegaMatcher On Card.
  • Includes fingerprint, iris, face, voice and palm print modalities.
  • Rolled, flat and latent fingerprint matching.
  • BioAPI 2.0 and other ANSI and ISO biometric standards support.
  • Multiplatform, scalable cluster architecture for parallel matching.
  • Effective price/performance ratio, flexible licensing and free customer support.

The charts below compare MegaMatcher SDK architectures for high performance AFIS

MegMatcher Algorithm Features and Capabilities
Performance numbers are provided for a PC with Intel Core i7-4771 processor (3.5 GHz).

MegaMatcher includes fingerprint, facial, speaker, iris and palm print recognition engines along with a fused algorithm for fast and reliable identification in large-scale systems.

The fingerprint, face, voice and iris identification algorithms may each be used separately to develop AFIS, automated face, speaker or iris identification systems.

The biometric software engines contain many proprietary algorithmic solutions that are especially useful for large-scale identification problems. These solutions were specifically developed for MegaMatcher, incorporating aspects of the VeriFinger, VeriLook, VeriSpeak and VeriEye algorithms. Some of these solutions are listed in the fingerprint, face, voice and iris biometric identification engine descriptions below.

MegaMatcher fingerprint template extraction and matching engine
  • Full MINEX Compliance. NIST has recognized MegaMatcher fingerprint algorithm as MINEX compliant and suitable for use in personal identity verification (PIV) program applications.
  • Rolled and flat fingerprints matching. The MegaMatcher fingerprint engine matches rolled and flat fingerprints between themselves. Typically, conventional "flat" fingerprint identification algorithms perform matching between flat and rolled fingerprints less reliably due to the specific deformations of rolled fingerprints. MegaMatcher allows flat-to-flat, flat-to-rolled or rolled-to-rolled fingerprint matching with a high degree of reliability and accuracy. The algorithm matches up to 200,000 flat fingerprint records per second on a single PC.
  • MegaMatcher includes fingerprint image quality determination, which may be used during enrollment to ensure that only the best quality fingerprint template will be stored in the database.
  • Template generalization is used to generate a better quality template from several fingerprints. Better quality templates result in a higher level of identification accuracy.
  • 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 or have deformations.
  • Adaptive image filtration algorithm eliminates noises, ridge ruptures and stuck ridges, and reliably extracting minutiae from even the poorest quality fingerprints in less than 1 second

MegaMatcher face template extraction and matching engine
  • Template generalization is used to generate a better quality template from several face images. Better quality templates result in a higher level of identification accuracy.
  • Tolerance to face position assures a level of enrollment convenience. MegaMatcher allows for 360 degrees of head roll. Head pitch can be up to 15 degrees in each direction from the frontal position. Head yaw can be up to 45 degrees in each direction from the frontal position. See technical specifications for more details.
  • Reliable face detection assures accurate enrollment from cameras, webcams and various scanned documents; faces may be enrolled from the scanned pages of passports or other types of documentation. When there are multiple faces present in a video or an image, they may be enrolled and processed simultaneously. Person's gender, facial feature points and basic emotions can be optionally detected.
  • Facial attributes recognition. MegaMatcher can be configured to detect certain attributes during the face extraction – smile, open-mouth, closed-eyes, glasses and dark-glasses.
  • Live face detection. A conventional face identification system can be tricked by placing a photo in front of the camera. MegaMatcher is able to prevent this kind of security breach by determining whether a face in a video stream is "live" or a photograph. See recommendations for live face detection for more details.
  • The biometric template record can contain several face samples belonging to the same person. These samples can be enrolled from different sources and at different times, thus allowing improvement in matching quality. For example a person might be enrolled with eyeglasses and without, or with different types of eyeglasses; with and without beard or moustache, etc.
MegaMatcher iris template extraction and matching engine
  • NIST IREX proven reliability. MegaMatcher iris matching engine is based on VeriEye, recognized by NIST as one of the most reliably accurate iris recognition algorithms available.
  • Fast matching. The iris matching speed is up to 200,000 comparisons per second on a single PC. See technical specifications for more details.
  • Robust iris detection. Irises are detected even when there are obstructions to the image, visual noise and/or different levels of illumination. Lighting reflections, eyelids and eyelashes obstructions are eliminated. Images with narrowed eyelids or eyes that are gazing away are also accepted.
  • Automatic interlacing detection and correction results in maximum quality of iris feature templates from moving iris images.
  • Correct iris segmentation is obtained even when perfect circles fail, the centers of the iris inner and outer boundaries are different, iris boundaries are definitely not circles and even not ellipses or iris boundaries seem to be perfect circles.

 

Technical Specifications
Performance numbers are provided for a PC with Intel Core i7-4771 processor (3.5 GHz).

All biometric templates should be loaded into RAM before identification, thus the maximum biometric templates database size is limited by the amount of available RAM.

  • Fingerprint scanners are recommended to have at least 500 ppi resolution and at least 1" x 1" fingerprint sensors. The specifications are provided for 500 x 500 pixels fingerprint images and templates extracted from these images.
  • Face capture cameras are recommended to produce at least 640 x 480 pixels images for reliable faces' detection. Face template extraction and matching speed is not dependent on the image size.
  • The minimal distance between eyes is 50 pixels for a face on image or video stream to perform face template extraction. 75 pixels or more recommended for better template extraction results.
  • Face recognition engine has certain tolerance to face posture:
    • head roll (tilt) – ±180 degrees (configurable);
      ±15 degrees default value is the fastest setting which is usually sufficient for most near-frontal face images.
    • head pitch (nod) – ±15 degrees from frontal position.
    • head yaw (bobble) – ±45 degrees from frontal position.
      ±15 degrees default value is the fastest setting which is usually sufficient for most near-frontal face images.
    The specifications are provided for the default roll and yaw values.
  • Iris capture cameras are recommended to produce at least 640 x 480 pixels images. The specifications are provided for these images.
  • Voice samples of at least 2-seconds in length are recommended to assure speaker recognition quality.
  • A minimum 11,025 Hz sampling rate, with at least 16-bit depth, should be used during voice recording.

See also the lists of basic recommendations for facial recognition and speaker recognition.

MegaMatcher biometric template extraction and matching algorithm is designed to run on multi-core processors allowing to reach maximum possible performance on the used hardware. More information


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