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ABIS NEURO
multi-modal biometric identification system
new generation

ALL IN ONE

Fast fingerprint identification

criminal AFIS

Face recognition

Iris recognition

Fast fingerprint identification

Fast fingerprint civil identification system based on algorithms derived from deep learning neural networks. The system generates fingerprint templates as a vector 512 bytes long. Comparison of templates is performed by measuring the distance between vectors in L2 metric. 1:1, 1:N, M:N (batch mode) comparison modes are supported.

Advantages

  • Super high identification speed - up to 100 million comparisons per second on a single state-of-the-art server/computer and up to 1 billion comparisons per second on 10 fingerprint identification requests.
  • Extremely low number of servers required on databases of tens and hundreds of millions records. One server supports a database of up to 100 million records.
  • Low identification error rates, on par with the best NIST test results.
  • Ability to work with fingerprint images obtained by photographing, including live fingerprints taken with smartphones.
  • Low sensitivity to changes in the scale of fingerprint images

Criminal AFIS

Identification system for criminal police works with the entire set of fingerprint information: contact and rolling fingerprints; latent prints and palm prints. A neural network-based identification algorithm is used for most queries. For latent prints of poor quality with a low number of features (minutiae), as well as for palms, combined type algorithms are used: a first pass with a neural algorithm and a second pass with a certified Sonda algorithm. When inputting information from paper fingerprints, neural algorithms are used to segment the prints, providing high quality that does not require operator correction.

Advantages

  • High identification speed and thus a drastic reduction in the number of servers/computers required.
  • Low value of identification error probabilities.
  • Sharp reduction in operator processing costs for fingerprint images, most processes are performed automatically, including the coding of most latent prints.

face recognition

Face recognition algorithm is based on neural networks and deep learning technologies for face detection and pattern extraction. The networks are trained on multinational datasets and have no significant bias towards any nation. Key features: face capture; biometric pattern generation; pattern matching 1:1, 1:N, M:N (batch mode). Templates are formed by the neural network in the form of a vector with a length of 1024 bytes.

Advantages

  • High comparison speed - up to 50 million comparisons per second on a single state-of-the-art server/computer.
  • Low identification error rates. Top five of NIST tests.
  • Low requirements for the face image. The distance between the pupils must be at least 20 pixels.

Iris recognition

Iris recognition algorithm is based on neural network and deep learning technologies. The algorithm includes an algorithm for iris detection, pattern formation and pattern matching 1:1, 1:N. The algorithm works with standard scanned iris images: near-infrared, preferably 640x480. The technology has greater resistance to artefacts such as eyelids, eyelashes, etc. compared to 'traditional' algorithms.

Advantages

  • High comparison speed - up to 100 million comparisons per second on a single modern server/computer.
  • Low identification error rates. Top five of NIST tests.