• The system must perform reliable identification with large databases, as biometrical identification systems tend to accumulate False Acceptance Rate with database size increase and using single fingerprint or face image for identification task becomes unreliable for large-scale application. Several biometrical samples should be used to increase identification reliability, and multi-biometrical technologies (i.e. collecting fingerprint and face samples from the same person) are often employed there for additional convenience.
• The system must show high productivity and efficiency, which correspond its scale:
  • System scalability is important, as the system might be extended in the future, so high productivity level should be kept by adding new units to the existing system.
  • Daily number of identification requests could be very high.
  • Identification request should be processed in a very short time (ideally – in real time), thus high computational power is required.
  • Support for large databases (tens or hundreds millions of fingerprints) is required.
  • General system robustness. The system must be tolerant to hardware failures, as even temporary pauses in its work may cause big problems taking into account the application size.
• The system must support major biometrical standards. This should allow using system generated templates or databases with the systems from other vendors and vice versa.
• The system must be able to match flat (plain) fingerprints with rolled fingerprints, as many institutions collect rolled fingerprint databases.
• The system must be able to work in the network, as in most cases client workstations are remote from the server with the central database.
• A forensic system must be able to edit latent fingerprint templates in order to submit latent fingerprints into AFIS for the identification.

Despite all these requirements, the system price should be as low as possible. Many existing AFIS (Automatic Fingerprint Identification Systems) are specialized for criminalistics or other particular applications and are quite expensive.

• Multibiometrics MegaMatcher includes fingerprint and facial recognition engines and allows integrators to use fused algorithm for better identification results or any of these engines separately. Identification reliability is a very important requirement for a large-scale system, thus usually two or more different biometrical samples from the same person are used to increase recognition reliability. Using MegaMatcher 2.0's multi-biometric technology, developers and integrators can create systems where both face and fingerprint can be scanned at the same time using inexpensive hardware, such as a fingerprint scanner and a simple webcam or photo scanner (for example, scanning a passport photo).
• Reliability :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 operation curves (ROCs) below show the reliability results for MegaMatcher 2.0.
  
  The first chart compares MegaMatcher 2.0 fingerprint identification engine reliability (green curve) with MegaMatcher 1.1 (red curve).
  The second chart compares MegaMatcher 2.0 face identification engine reliability (blue curve), fingerprint identification engine (green curve) and the fused face-fingerprint algorithm (red curve).
  
  
  Click to enlarge
  
  
  Click to enlarge
  
  These ROCs show that large-scale automated biometrical identification system based on MegaMatcher provides high identification reliability when using fingerprints, and using multi-biometrical identification results in significant reliability increase, allowing to reach almost 0% FRR.
• Matching speed. MegaMatcher is able to match up to 400,000 templates per second running the fused algorithm on a stand-alone PC with 3GHz CPU. MegaMatcher's facial recognition engine is able to match up to 500,000 faces per second, and the fingerprint recognition engine matches up to 60,000 fingerprints per second. The matching speed could be significantly increased by using the PC-based cluster.
• MegaMatcher includes computer cluster software for performing parallel matching, which allows to reach high productivity and efficiency:
  • The effective matching speed increases proportionally to the number of cluster's nodes and can be scalable to achieve the necessary system performance. For example, a cluster with 10 nodes is able to match up to 600,000 fingerprints per second, a cluster with 100 nodes – up to 6 millions fingerprints per second etc. Such scalable architecture allows to keep up the fast system's response if its size becomes larger.
  • Large number of identification requests could be processed by the cluster. Suppose, there is a database with 10 million fingerprints and a cluster of 100 nodes (PCs with 3GHz CPU). Depending on the problem this cluster will be able to process from 8,000 to 50,000 requests per day with the given database.
  • Fast request processing. The cluster's architecture allows to scale it to achieve real-time processing of the identification request.
  • The cluster is able to handle databases of a practically unlimited size.
  • Computer cluster is fault-tolerant, so in case of cluster element's fault, the matching speed slightly decreases, but the cluster's work remains uninterrupted.
• Megamatcher supports biometric standards: ANSI/NIST ITL-1-2000 and ANSI/INCIST 378 2004 are supported. Therefore, MegaMatcher fingerprint templates could be exported to another identification system and vice versa. Additionally, MegaMatcher supports WSQ fingerprint image storage format.
• The technology allows to match 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.
• MegaMatcher includes network support, as components of MegaMatcher are intended to be distributed on the network.
• Effective price/performance ratio. MegaMatcher uses a PC and can work with Microsoft Windows and Linux operating systems. This configuration provides the most price/performance effective computational units for all components of the system. Therefore, developing with MegaMatcher SDK means that the system price will be reasonable for both software and hardware parts.
• MegaMatcher is fully compatible with other Neurotechnologija products: VeriFinger, VeriLook, FingerCell and FaceCell.

• 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, which identifies fingerprints even if they are rotated, translated and have deformations.
• MegaMatcher algorithm is able to match rolled fingerprints, flat fingerprints, and also rolled with flat between themselves. Due to the specific scanning technique (rolling from nail to nail) rolled fingerprints usually have much bigger deformation than those scanned using the "flat" technique. MegaMatcher matches rolled fingerprints very well, as it is tolerant to fingerprint deformations.
• MegaMatcher can use database entries which were pre-sorted using certain global features and matches about 60,000 fingerprints per second using the pre-sorted records. 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 allows to eliminate 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 Pentium 4, 3 GHz processor).

• 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.
• Biometrical 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 to improve matching quality. For example a person could be enrolled with and without eyeglasses or with different eyeglasses, with and without beard or moustache, etc.