Hybridization of Particle Swarm Optimization and Firefly

by Thesis Work

Hybridization of Particle Swarm Optimization and Firefly

For the classification of satellite image the main part is feature optimisation because of without appropriate feature set we cannot classify, so we use hybrid technique of optimisation with Particle Swarm Optimisation and Firefly Algorithm.

Fingerprints biometric| Thesis help in chandigarh

by Thesis Work

Fingerprints are one of the most studied biometric traits and the most widely used in civil and forensic recognition systems In the field of criminal investigation, civilian, government and commercial devices applications such as passport, licence card, security device etc., use of fingerprint technology is employed. Fingerprints of the human being are unique and they can never be the identical. The fingerprint of an individual is unique and remains unchanged over a lifetime. A fingerprint is formed from an impression of the pattern of ridges on a finger. A ridge is defined as a single curved segment, and a valley is the region between two adjacent ridges. Ridge endings are the points where the ridge curve terminates, and bifurcations are where a ridge splits from a single path to two paths at a Y-junction .Ridges and valleys or bifurcations can be captured from a finger by inked press, capacitive or optical sensors, etc.

A minutia detected in a fingerprint image can be characterized by a list of attributes that includes: 1) the type of minutia (ending or bifurcation), 2) the minutia position with respect to the image frame, and 3) the minutia direction which is defined as the angle that the ridge associated with the minutia makes with the horizontal axis.

FINGERPRINT RECOGNITION PROCESS

The fingerprint recognition problem can be grouped into two sub-domains: one is fingerprint verification   and the other is fingerprint identification.Fingerprint verification  is to verify the authenticity of one person by his fingerprint. The user provides his fingerprint together with his identity information like his ID number. The fingerprint verification system retrieves the fingerprint template according to the ID number and matches the template with the real-time acquired fingerprint from the user.

Fingerprint identification is to specify one person’s identity by his fingerprints. Without knowledge of the person’s identity, the fingerprint identification system tries to match his fingerprints with those in the whole fingerprint database. It is especially useful for criminal investigation cases.

TECHNIQUES FOR FINGERPRINT RECOGNITION

Minutiae Matching:

Minutiae are major features of a fingerprint, using which; comparisons of one print with another can be made Minutiae include:

  • Ridge ending – the abrupt end of a ridge
  • Ridge bifurcation – a single ridge that divides into two ridges
  • Short ridge, or independent ridge – a ridge that commences, travels a short distance and then ends
  • Island – a single small ridge inside a short ridge or ridge ending that is not connected to all other ridges
  • Ridge enclosure – a single ridge that bifurcates and reunites shortly afterward to continue as a single ridge
  • Spur – a bifurcation with a short ridge branching off a longer ridge
  • Crossover or bridge – a short ridge that runs between two parallel ridges
  • Delta – a Y-shaped ridge meeting
  • Core – a U-turn in the ridge patter

Pattern Based Algorithm

Pattern based algorithms compare the basic fingerprint patterns (arch, whorl, and loop) between a previously stored template and a candidate fingerprint. This requires that the images can be aligned in the same orientation. To do this, the algorithm finds a central point in the fingerprint image and centres on that. In a pattern-based algorithm, the template contains the type, size, and orientation of patterns within the aligned fingerprint image. The candidate fingerprint image is graphically compared with the template to determine the degree to which they match.

Correlation-Based Matching:

Let I(∆x, ∆y, θ) represent a rotation of the input image I by an angle θ around the origin (usually the image center) and shifted by Δx and Δy pixels in directions x and y, respectively. Then the similarity between the two fingerprint images T and I can be measured as

S(T,I)=                                                              (1)

where CC(T,I)=TÙT I is cross-correlation between T and I. The cross-correlation is a well known measure of image similarity and the maximization in (1); it allows us to find the optimal registration.

APPLICATIONS
  • Logical access control, for example there exist numerous fingerprint reader devices and software for access control to personal computers.
  • Physical access control, for example locks with a fingerprint reader.
  • Fingerprint attendance systems for time and attendance management.
  • Biometric alternative to loyalty card systems.