OPTIMIZATION WITH SWARM OF PARTICLES FOR FORENSIC IDENTIFICATION

Resumen

In this work we present the analysis and identification of people through fingerprints, using the Fourier transform and the swarm of particles. Currently fingerprint processing is widely used for a variety of applications, this allows the treatment of information to be personalized and focused. The Fourier transform was used to differentiate people's footprints, producing a frequency spectrum that is then clustered. Search optimization by particle swarm consists of an
iterative algorithm based on a population of individuals, in which each one flies over the decision space in search of optimal solutions. The forensic identification is a process currently used for the recognition of people involved in legal processes, therefore it is essential the optimal classification of data for the correct recognition of identity. In this work two methods are proposed; Fourier analysis and particle swarm, to guarantee the lowest percentage of error in the classification process.