Server platform for fingerprint recognition

Neurodactyl server platform provides easy implementation of fingerprint recognition features using simple RESTful web-API. The platform is based on microservices architecture, which allows flexible system scaling and load balancing. Neurodactyl server platform can be deployed as cloud fingerprint recognition system with a sandbox for each user. The platform utilizes Neurodactyl fingerprint recognition SDK.

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1

Fingerprints detection
The platform detects fingerprints on photo images and scans (250 dpi and higher). The detector returns 2 landmarks and bounding boxes for each detected fingerprint. Number of fingerprints/fingers on an image is not limited. Left/right hand detection based on fingerprint analysis (optional).
2

Biometric template extraction
The platform converts an image into compact descriptor, describing unique features of a fingerprint. All templates extracted from photo images and scans are compatible and have standard size - 512 bytes.
3

Matching
The platform compares templates against each other in different modes: 1:1, 1:N, M:N (batch mode) and returns similarity score (native values equal to -logFAR and %).
4

User authorization
Users are authorized with JWT (json web token) and can have fully separated sandboxes for their enrolled data

1

Microservices architecture
All features of Neurodactyl server platform operate as microservices, which helps to build resilient and scalable fingerprint recognition system. All microservices are containerized and can be deployed with container orchestration system like Kubernetes, etc.
2

Easy scale up
Detection and extraction features are separated as microservices, which you can flexibly increase, when total load of the system - number of incoming requests - is increasing. The load will be automatically distributed and balanced between all services of detection and template extraction.
3

Automatic batch collection
Neurodactyl fingerprint recognition SDK allows to use batches to increase throughput on GPU. Neurodactyl server platform automatically collects batches of optimum size in order to provide faster processing of incoming images and best utilization of available hardware.
4

Sandboxes
The platform allows to create fully isolated sandboxes. Sandboxes can be assigned to users, so they can have fully isolated enrollment databases for identification without interaction or having access to other user's data.

Interface: RESTful web-API

Database: MongoDB

Service discovering: Zookeeper

OS: Linux (amd64)

Minimum HW requirements: CPU Intel or AMD with AVX2 instructions, 8 GB RAM, 4 GB free space on a drive.

HW requirements for a particular use case should be calculated for a project and depend on: size of enrollment database, number of incoming images per 1 s, types and resolutions of incoming images.

Supported GPU: NVidia GPUs starting from Pascal architecture or later, at least 6 GB RAM.

Image requirements:

For scans - 250 dpi and higher, rolls, flats and latents are supported.
For photo images - quality of images (resolution and sharpness) must provide visible fingerprints patterns. Size of a phalanx must be at least 200 pixels. We recommend to use Neurodactyl Mobile capture SDK for image acquisition with mobile phones.

Supported image formats: png, jpeg, bmp, wsq and others.

Neurodactyl server platform is licensed per:

Number of enrolled images (size of N in the matching gallery)
Number of GPUs
Number of servers/machines

Price calculation can be done based on following project information:

Size of the enrollment database
Workload: number of incoming images for detection and template extraction per 1 s
Types of incoming images: photo/scans, resolution
CPU or GPU is used for processing

If you want to know more about licensing and pricing, please contact us info@neurodactyl.com

Test license request

If you want to get evaluation license, please fill the form below or send us an email to: info@neurodactyl.com