Saturday, August 30, 2008  | 
Leverage Existing Infrastructure

ClassifEye has developed a robust server-based backbone that integrates with mobile handsets and server-based applications. ClassifEye’s technology enables identification, authentication and non-repudiation for m-Commerce and m-Banking markets using existing hardware and proprietary software. The technology can be integrated into a variety of solutions and easily deployed to wide customer bases. As a result, end-user security can be delivered at a level that satisfies banks and merchants, as well as consumers, enabling cost savings, improved services, and further deployment of new services and applications.

ClassifEye’s software is loaded on the handset, either as a preload to new handsets or as a download to existing handsets, and is integrated within existing identity management applications and network architectures for server based applications; the Classifeye Server is designed to handle large volumes of authorization requests. Following illustrates ClassifEye’s authentication process. The user enrolls by using the cell phone camera to register his biometric signature/identification, as extracted from a series of fingerprint images. The signature is a digital code based on features that are extracted from the images and uniquely identify a particular user. Upon demand, the user identifies and authenticates himself, in real time, by using a cell phone camera to authenticate his fingerprint/digital signature against the feature list/digital code extracted upon enrollment.


 
ClassifEye Network
Features

ClassifEye’s fingerprint authentication technology successfully overcomes image processing challenges, hardware limitations, and security hurdles, including:

  • 2-d imaging of 3-d objects: ClassifEye’s algorithms and software were specifically developed for two-dimensional imaging of three-dimensional objects.
  • Poor quality imagers: ClassifEye’s algorithms and software can analyze images of relatively poor quality, consistent with many optical systems and imaging chips in web cams and cellular camera phones.
  • Variable light conditions: ClassifEye’s algorithms and software compensate for variable environmental conditions, and work in relatively low light conditions.
  • Wide gamut of user behaviors: ClassifEye’s algorithms and software are not very sensitive to ergonomic changes, such as physical position of the camera, and operates under a wide range of user behaviors.
  • Limited HW resources for authentication: ClassifEye’s algorithms and software occupy a limited footprint and require relatively low processing speeds.
  • Immunity against image fraud: ClassifEye’s technology distinguishes between real fingers and images of real fingers.
  • Re-use of “old” fingerprints: ClassifEye’s technology ensures uniqueness of each transaction to safeguard against stored images (i.e., “old fingerprints”) and/or hacked authorizations. ClassifEye is close to achieving a False Acceptance Rate (FAR) of 1:100.000 with a False Reject Rate (FRR) of 5%.
 
                                                            
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