* Biometric Finger Print Scanner
      Biometric finger print scanner is developed by Digital Persona. A well known biometric finger print scanner manufacturer in the United States. Digital Persona was founded by scientists from Caltech and MIT and founders of Logitech. From inception, Digital Persona’s mission was to make security convenient, reliable and enjoyable for end-users.  To accomplish this, Digital Persona developed state-of-the-art fingerprint recognition technology that could be brought to market at an affordable price.


                                                                        
* Barcode Reader (optional)
     Any brand or type of barcode scanner can be connected to the system as long as it can emulate the keyboard and preferably has "triggerless" operation. The system has been tested using a Barcode slot reader. The 1022 Barcode Slot Reader is a flexible reader with visible red light or infra-red light sources capable of reading poor quality printed barcodes, invisible barcodes or security laminated barcodes. Its plastic housing makes the unit very light and provides excellent protection for its optical system against performance degradation in harsh environments.

Biometric Finger Print Recognition Engine
     Neurotechnologija has developed fingerprint identification algorithm. VeriFinger has the capabilities of the most powerful fingerprint recognition algorithms. Human fingerprints are unique to each person and can be regarded as a sort of signature, certifying the person's identity. The most famous application of this kind is in criminology. However, nowadays, automatic fingerprint matching is becoming increasingly popular in systems which control access to physical locations, computer/network resources, bank accounts, or register employee attendance time in enterprises.

Why Verifinger?

In 1998 Neurotechnologija developed VeriFinger, a fingerprint identification algorithm, designed for biometric system integrators. Since that time, Neurotechnologija has released 10 algorithm versions, with the current version, VeriFinger 5.0, providing the the most powerful fingerprint recognition algorithms to date:

• Reliability. Even earlier VeriFinger fingerprint identification algorithm versions consistently have shown some of the best results for reliability in several biometric competitions, including the International Fingerprint Verification Competition (FVC2004, FVC2002 and FVC2000) and the National Institute of Standards & Technology (NIST) Fingerprint Vendor Technology Evaluation (FpVTE 2003), where Neurotechnologija ranked among the top five companies for accuracy in single-finger tests. VeriFinger 5.0 provides major reliability improvements over these earlier versions.
• Fingerprint matching speed is one of the highest among the competing identification algorithms. Fingerprint enrollment time is 0.2-0.4 sec., and VeriFinger can match 40,000 fingerprints per second in 1:N identification mode. To confirm these results with your data, please try VeriFinger algorithm demo (see section below).
• VeriFinger algorithm includes image quality determination and features generalization which can be used during fingerprint enrollment to ensure that only the best quality fingerprint template will be stored into database.
• VeriFinger is offered for a competitive price. Developers can select from several types of SDK and licensing models. Each of these kits and models is intended for specific needs, and developers always can make an upgrade by paying the difference between the current and more powerful SDK.

The VeriFinger fingerprint recognition algorithm follows the commonly accepted fingerprint identification scheme, which uses a set of specific fingerprint points (minutiae). However, it contains many proprietary algorithmic solutions, which enhance the system performance and reliability. Some of them are listed below:

• The adaptive image filtration algorithm allows to eliminate noises, ridge ruptures and stuck ridges, and extract minutiae reliably even from poor quality fingerprints, with a processing time of about 0.2 - 0.4 seconds (all times are given for a Pentium 4, 3 GHz processor). You can look at the screenshot of the VeriFinger demo application showing an example of initial fingerprint image (left window), and the same image after the noise filtering and processing by VeriFinger (right window), with minutiae positions and directions marked by red circles and lines.
• VeriFinger functions can be used in 1:1 matching (verification), as well as 1:N mode (identification).
• VeriFinger includes a fast template matching algorithm that is tolerant to fingerprint translation, rotation and deformation. VeriFinger's proprietary fingerprint matching algorithm allows it to match up to 40,000 fingerprints per second and identify fingerprints even if they are rotated, translated and have only 5 - 7 similar minutiae (usually fingerprints of the same finger have 20 - 40 similar minutiae).
• VeriFinger does not require the presence of the fingerprint core or delta points in the image, and can recognize a fingerprint from any part of it.
• VeriFinger 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 fairly 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 matching speed increases.
• VeriFinger has the fingerprint enrollment with features generalization mode. This mode generates the collection of the generalized fingerprint features from a set of fingerprints of the same finger. Each fingerprint image is processed and features are extracted. Then the features collection set is analyzed and combined into a single generalized features collection, which is written to the database. This way, the enrolled features are more reliable and the fingerprint recognition quality considerably increases.