Finger Print Technalogy

FINGERPRINT A fingerprint is an impression of the friction ridges of all or any part of the finger. A friction ridge is a raised portion of the epidermis on the pal mar (palm and fingers) or plantar (sole and toes) skin, consisting of one or more connected ridge units of friction ridge skin. These ridges are sometimes known as “dermal ridges “or “dermal papillae’. Fingerprint may be deposited in natural secretions from the eccrine glands present in friction ridge skin (secretions consisting primarily of water) or they may be made by ink or other contaminants transferred from the peaks of friction skin ridges to a relatively smooth surface such as a fingerprint card. The term fingerprint normally refers to impressions transferred from the pad on the last joint of fingers and thumbs, through fingerprint cards also typically record portions of lower joint areas of the fingers. 2.1 BENEFITS OF FINGERPRINT TECHNOLOGY Today fingerprint devices are by far the most popular form of biometric security used, with a variety of systems on the market intended for general and mass-market usage. Long gone are the huge bulky fingerprint scanners; now a fingerprintscanning device can be small enough to be incorporated into a laptop for security.

2.2 FINGERPRINT SCANNING An individual’s fingerprint is scanned to identify 10 to 26 unique points of the finger, and a unique number is assigned to it. The original fingerprint image is not saved, but the fingerprint algorithm is stored. It cannot be used by law enforcement for future identification purposes.

The fingerprint is acquired from a fingerprint scanner.

Image is improved (better contrast and distinctness).

Noise and defects are eliminated.

Figure: 2.1 scanned finger print

Minutiae are identified. Ending Bifurcation Figure: 2.2 Analyzed fingerprint Fingerprint features are detected and analyzed. Fingerprint search on database is made based on some measures; so polygons are determined connecting 3 minutiae. Thus, internal angles, sides and each minutia angle are computed. These measures are invariant to rotation and translation. This method allows that a desired fingerprint can be localized on database even with position variation (displacement and rotation) in relation to the found fingerprint.

Desired fingerprint Figure: 2.3 Fingerprint

Found Fingerprint

2.3 OBTAINING FINGERPRINT Every fingerprint can be broken down into two basic features, called ridges and valleys. By examining these characteristics; it is possible to extract data from raw fingerprints and store it in a computer database for future comparisons. Images can be captured using one of several devices, including: • Optical Scanners • Thermal Scanners • Capacitive (solid-state) Scanners 2.4 EXTRACTING FINGERPRINT There are currently two accepted methods for extracting this data. (1)Minutia-based (2)Correlation-based Minutia–based is the more microscopic of the two, locating ridge branches and endings and assigning them and XY-coordinate that is then stored in a file. In the Correlation-based method looks the overall pattern of ridges and valleys. Instead of looking for tiny minutia points, the locations of whorls, loops and arches and the directions that they flow in are extracted and stored. Neither method actually keeps the captured image; only the data is kept, therefore making it impossible to recreate the fingerprint. Both methods have their drawbacks, though minutia-based comparison requires

that the fingerprint image be high quality, with dirt, water, scars and cuts having a significant impact on proper identification. On the other hand, correlation-based comparisons can be affected by image translation and rotation. 2.5 FINGERPRINT DATA TEMPLATE SIZE The fingerprint requires one of the largest data templates in the biometric field. The finger data template size can range anywhere from several hundred bytes to over 1,000 bytes depending upon the levels of security that is required and the method that is used to scan ones fingerprint. The good news is that there are algorithms being developed that can “enhance” images in order to reduce distortion and false data while minimizing file size. 2.6 FINGERPRINT RECOGNITION Fingerprint-based identification can be placed into two categories. Minutiae-based matching (Analyzing the local structure) and Global pattern matching (Analyzing the global structures).

Currently the computer aided fingerprint recognition is using the minutiae-based matching. Minutiae points are local ridge characteristics and appear as either a ridge ending or a ridge bifurcation. The uniqueness of a fingerprint can be determined by the pattern of the ridges and the valleys a fingerprint’s made of. A complete fingerprint consists of about 100 minutiae points in average. The measured fingerprint area consists in average of about 30-60 minutiae points depending on the finger and on the sensor area. These minutiae points are represented by a cloud of dots in a coordination system. They are stored together with the angle of the tangent of a local minutiae point in a fingerprint-code or directly in a reference template. A template can consist of more than one fingerprint-code to expand the amount of information and to expand the enrolled fingerprint area. In general this leads to higher template quality and therefore to a higher similarity value of the template and the sample. The template size varies from 100 to 1500 bytes depending on the algorithm and the quality of a fingerprint. Nevertheless, very rarely there are fingerprints without any minutiae-points that leads to a failure to enroll (FER=Failure to Enroll Rate).it is also difficult to extract the minutiae points accurately when the fingerprint has got a low quality. 2.7 ADVANTAGES OF USING FINGERPRINT • Prevents unauthorized use or access

• Adds a higher level of security to an identification process • Eliminates the burden and bulk of carrying ID cards or remembering Pins • Heightens overall confidence of business processes dependent on personal identification.