Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features

Journal Title: Journal of Information Systems and Telecommunication - Year 2013, Vol 1, Issue 2

Abstract

Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.

Authors and Affiliations

Sara Motamed, Ali Broumandnia, Azamossadat Nourbakhsh

Keywords

Related Articles

A Unicast Tree-Based Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks

The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be ackn...

Facial Expression Recognition Using Texture Description of Displacement Image

In recent years, facial expression recognition, as an interesting problem in computer vision has been performed by means of static and dynamic methods. Dynamic information plays an important role in recognizing facial ex...

Low Complexity Median Filter Hardware for Image Impulsive Noise Reduction

Median filters are commonly used for removal of the impulse noise from images. De-noising is a preliminary step in online processing of images, thus hardware implementation of median filters is of great interest. Hence,...

A Stochastic Lyapunov Theorem with Application to Stability Analysis of Networked Control Systems

The source of randomness in stochastic systems is an input with stochastic behavior as treated in the existing literature. Special types of stochastic processes such as the Wiener process or the Brownian motion have serv...

Analysis of expert finding algorithms in social network in order to rank the top algorithms

The ubiquity of Internet and social networks have turned question and answer communities into an environment suitable for users to ask their questions about anything or to share their knowledge by providing answers to ot...

Download PDF file
  • EP ID EP190111
  • DOI 10.7508/jist.2013.02.002
  • Views 122
  • Downloads 0

How To Cite

Sara Motamed, Ali Broumandnia, Azamossadat Nourbakhsh (2013). Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features. Journal of Information Systems and Telecommunication, 1(2), 79-88. https://europub.co.uk/articles/-A-190111