FingerCell, Embedded Fingerprint Recognition Technology

Content of this page

• Why FingerCell?
• Algorithm
• Specifications
• Demo
• Related Products

Why FingerCell?

The FingerCell algorithm, developed on the VeriFinger basis, is designed for embedded biometric systems developers. The algorithm has certain capabilities:

• Atmel
  Fingerchip
  
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  DigitalPersona
  U.are.U 4000
  
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  Reliability. As FingerCell is intended for embedded devices, it uses a faster and less powerful fingerprint noise filtration algorithm with a slightly higher False Rejection Rate than a PC running the VeriFinger algorithm. However, the FingerCell algorithm still produces a decent level of recognition reliability, which is acceptable for embedded devices. Receiver operation characteristic (ROC) curves obtained in testing with two scanner databases compare FingerCell 2.1 (green) and VeriFinger 5.0 (red) reliability under the same conditions.
• Low speed processors are supported. For example, a 75 MHz ARM7 processor performs verification in about 2 seconds when FingerCell algorithm is used.
• Identification ability. As FingerCell is developed on the VeriFinger basis, it is suitable not only for fingerprint verification (1:1 matching), but also for identification (1:N matching). FingerCell can match up to 700 fingerprints per second in 1:N identification mode on 200 MHz ARM family CPU.
• Image processing speed. Fingerprint image processing time is less than 1 second on 200 MHz ARM processor, which is acceptable for embedded systems.
• Compact software. Compiled code and internal data arrays require only 400 Kb of memory and therefore can be implemented in low memory microchips, thus reducing hardware costs.
• Available for various project scales as FingerCell 2.1 Library EDK or FingerCell 2.1 source code EDK.
• Portability. FingerCell Embedded Development Kit is designed for easy implementation into very various and specific applications. The algorithm's source code is written in ANSI C and is sensor independent; therefore it can be ported to various platforms and hardware.
• Embedded and PC-based multi-biometric capable technologies from the same vendor. Combined with our other technologies, FingerCell could be used in developing these advanced systems:
  • Mixed embedded/PC systems, using FingerCell EDK together with VeriFinger Standard or Extended SDKs.
  • Multi-biometric embedded systems, using FingerCell EDK together with FaceCell EDK.
  • Complex multi-biometric embedded/PC systems, using a combination of FingerCell EDK, FaceCell EDK, VeriFinger SDK and VeriLook SDK.

Algorithm

The FingerCell algorithm is similar to the VeriFinger algorithm and includes these features:

• FingerCell is fully tolerant to fingerprint translation, rotation and deformation. Such tolerance is achieved by our proprietary fingerprint matching algorithm.
• FingerCell does not require the presence of fingerprint core or delta points in the image and can recognize a fingerprint from any part of it.
• FingerCell has fingerprint enrollment with features generalization mode. This mode generates a collection of the generalized fingerprint features from a collection of fingerprints of the same finger. Each fingerprint image is processed and features are extracted. Then the collection of features is analyzed and combined into a single generalized features collection which is written to the database. This way, enrolled minutiae are more reliable and the fingerprint recognition quality considerably increases using this enrollment mode.
• FingerCell can use database entries which were pre-sorted using certain global features. 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 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 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 correspondingly, the effective matching speed increases.
• The template extraction is adapted for low speed embedded processors to provide fast image processing and feature extraction.

Specifications

Please note, that these specifications were determined on device with 200 MHz ARM family processor.

FingerCell Algorithm Demo

Neurotechnology offers a downloadable FingerCell demo application for PC that allows to evaluate the FingerCell 2.1 algorithm. The application is intended for running on PCs with Microsoft Windows 2000/XP/2003/Vista.
Internet connection is not required to run the demo application.

FingerCell 2.1 EDK 30-day trial trial is also available for downloading.

Related Products

These products are based on the FingerCell 2.1 algorithm:

• FingerCell 2.1 Library EDK
• FingerCell 2.1 Source Code EDK
• FingerCell 2.1 EDK trial