MegaMatcher SDK

Large-scale AFIS and multi-biometric identification

MegaMatcher is designed for large-scale AFIS and multi-biometric systems developers. The technology ensures 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.

Features and Capabilities

  • Proven in national-scale projects, including passport issuance and voter deduplication.
  • NIST MINEX-compliant fingerprint engine, NIST IREX proven iris engine.
  • High performance matching for national-scale systems 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.
  • Effective price/performance ratio, flexible licensing and free customer support.

MegaMatcher technology for large-scale Automated Biometric Identification Systems was introduced in 2005. Since that time the technology has been constantly improved with more than 10 major and minor versions released to date.

MegaMatcher technology is available as multiplatform SDK, which 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 biometric software engines contain many proprietary algorithmic solutions that are especially useful for large-scale identification problems. 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, dark-glasses, beard and mustache.
  • Age estimation. MegaMatcher can optionally estimate person's age by analyzing the detected face in the image.
  • 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. The liveness detection can be performed in passive mode, when the engine evaluates certain facial features, and in active mode, when the engine evaluates user's response to perform actions like blinking or head movements. 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 mustache, etc.

MegaMatcher voice template extraction and matching engine

  • Text-dependent voice matching engine determines if a voice sample matches the template that was extracted from a specific phrase. During enrollment, one or more phrases are requested from the person being enrolled. Later that person may be asked to pronounce a specific phrase for verification. This method assures protection against the use of a covertly recorded random phrase from that person.
  • Two-factor authentication with a passphrase is performed when a person is asked to say a unique phrase (such as passphrase or an answer to a "secret question" that is known only by the person being enrolled). The overall system security increases as both voice authenticity and password are checked.
  • Text-independent voice matching engine uses different phrases for user enrollment and recognition. This method is more convenient, as it does not require each user to remember the passphrase. It may be combined with the text-dependent algorithm to perform faster text-independent search with further phrase verification using the more reliable text-dependent algorithm.
  • Automatic voice activity detection. The engine is able to detect when users start and finish speaking.
  • Liveness detection. A system may request each user to enroll a set of unique phrases. Later the user will be requested to say a specific phrase from the enrolled set. This way the system can ensure that a live person is being verified (as opposed to impostor who uses voice recording).
  • Several voice records with the same phrase may be stored to improve speaker recognition reliability. Certain natural voice variations (i.e. hoarse voice) or environment changes (i.e. office and outdoors) can be stored in the same template.

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.