Wednesday, September 29, 2004

APPLICATION OF BIOMETRICS IN HUMAN IDENTIFICATION

1P. SURESH KUMAR, 1B.ANAND, and 2N. VAIJAYANTHI

  1. School of Engineering & Technology, Bharathidasan University, Trichy.
  2. J.J. College of Engineering & Technology, Trichy.

Biometrics is the measurement of physical, biological or behavioral characteristics of an individual- the face, finger, hand, iris and voice signature used to create a unique identifier which can be electronically stored, retrieved and compared for identification purposes. In simple words, biometrics means using the body as a password. There are various biometric techniques available.

Finger Prints: When you place your finger on the pad of the optical scanner, sensors look for a pattern of minutiae in the print, and match it with the previously recorded print. A single rolled fingerprint may have as many as 100 or more identification points than can be used for identification purposes. When the finger touches the silicon sensor, the pattern of ridges and valleys is determined during a cycle of charging and discharging the capacitor array. The data are scanned at 500 dots per inch, converted to digital form and a map of unique finger characteristics is created on the basis of minutia detection. The map is then compared with a data bank of known prints.

Iris Scanning : As an identifying body part, the human-iris, the coloured protein of eye has several advantages. It is an integral part of the body, so it is not amenable to easy modification. Unlike fingerprints, the iris can be imaged from one meter away. Yet, like fingerprints iris patterns are unique to individuals, inclusive, non exception identical twins. These patterns are stable throughout life. The iris algorithm precisely locates the outer and inner borders of the iris, detects and excludes the cycloids if they cover a part of the iris. The system uses a mathematical technique called wavelet technique analysis to translate the image of the iris into a 512-bit pattern. Wavelet analysis is a mathematical relative of the Fourier analysis, and it breaks down an image into a set of specially limited waves. This pattern, which is called the iris code, is defined in a coordinate system that is invariant to change in pupil contraction and to the size of the image itself. Once an iris code is prepared, the algorithm compares a specific code against a group of codes previously stored in the computer. If there is no match, the fraction of disagreement should be close to 0.5, otherwise it should be close to 0.

Face Off : Facial recognition works by isolating human faces in still pictures and measuring an array of facial characteristics, such a the geometry of a person’s eyes, mouth and nose. Using a proprietary algorithm, the system compares the image to database stored photos for probability ranked matches, as certain facial aspects do not change, even with age or weight fluctuations.

Voice Biometrics and Voice Prints : A voice print is a digital representation of the unique features of an individuals voice. Voices differ due to a number of physiological characteristics such as voice chords, trachea, nasal passages, movement of the tongue inside one’s mouth to produce certain sounds, etc. The combination of these characteristics is analyzed and identified as unique for every individual. A voiceprint is not a sound file (recording) of the individual and therefore cannot be replaced as a recording by an imposter.
Multi-Biometrics: Fingerprint-mosaicking : The reduced contact area offered by solid state fingerprint sensors do not provide sufficient information (ex. -Minutia) for high accuracy user verifications. Multiple impression of the same finger acquired by these sensors, may have only a small region of overlap thereby affecting the matching performance of the verification system. To deal with this problem, a fingerprint-mosaicking scheme that constructs a composite fingerprint image using multiple fingerprints is used.

In this algorithm, two impressions of a finger are initially aligned using the corresponding minutia points. The alignment is used by the well known interactive closet point algorithm (ICP) to compute a transformation matrix that defines the special relationship between the two impressions. The transformation matrix is used in two ways. (a) The two impressions are stitched together to generate a composite image. Minutiae points are then detected in this composite image. (b) The minutia maps obtained from each of the individual impressions are integrated to create a larger minutia map. The availability of a composite template improves the performance of the fingerprint matching system.

Dental Biometrics : Given a dental record, usually as a postmortem (PM) radiograph, we need to search the database of ante mortem (AM) radiographs to determine the identity of the persons associated with the PM image. Semi-automatic method is used to extract shapes of the teeth from the AM and PM radiographs.

A ranking of matching scores is generated based on the distance between the AM and PM tooth shapes. Dental images based on tooth shapes and their relative position is a feasible method for human identification.

Iris scanning & finger print imaging has been found to be effective when compared with other biometric techniques. Further to substantiate the Connecticut welfare programme ID cards, certain photograph & signature with encoded finger print. In future we may encounter newer technologies in biometrics for better human identification.


Map of Unique finger characteristics


Silicon

Sensor


Fig. 1 Fig. 2 Fig. 3 Fig. 4

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