United States Megamatcher Tender

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 are based on deep neural networks and 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 ENGINEFull 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 200000 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.Spoof fingerprint detection. A deep learning based scanned fingerprint image classification is used to separate livenon-live fingerprints to detect finger presentation attack. This feature covers spoofing attempts performed with ecoflex wood glue latex and gelatin and is useful for fraud identification.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. Also the matching algorithm has a special mode for the cases when some fingerprint records have incorrect resolution.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 ENGINETemplate 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. Persons gender facial feature points and basic emotions can be optionally detected. Also partially occluded faces i.e. persons wearing face masks or respirators can be recognized without separate enrollment.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 persons 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 or single frame 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 users 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 ENGINEText-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.