MULTIMODAL BIOMETRIC RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES

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MOTAMED SARA; BROUMANDNIA ALI; NOURBAKHSH AZAMOSSADAT

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Journal Paper

Paper Information

Journal: Journal of Information Systems and Telecommunication Year:2013 | Volume:1 | Issue:2 Page(s): 79-87

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Title

MULTIMODAL BIOMETRIC RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES

Author(s)

MOTAMED SARA | BROUMANDNIA ALI | NOURBAKHSH AZAMOSSADAT | Issue Writer Certificate

Keywords

BIOMETRIC

GENETIC ALGORITHM (GA)

PARTICLE SWARM OPTIMIZATION (PSO)

DISCRETE COSINE TRANSFORM (DCT)

DISCRETE WAVELET TRANSFORM (DWT)

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.

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APA: Copy

MOTAMED, SARA, BROUMANDNIA, ALI, & NOURBAKHSH, AZAMOSSADAT. (2013). MULTIMODAL BIOMETRIC RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), 1(2), 79-87. SID. https://sid.ir/paper/332636/en

Vancouver: Copy

MOTAMED SARA, BROUMANDNIA ALI, NOURBAKHSH AZAMOSSADAT. MULTIMODAL BIOMETRIC RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES. JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST)[Internet]. 2013;1(2):79-87. Available from: https://sid.ir/paper/332636/en

IEEE: Copy

SARA MOTAMED, ALI BROUMANDNIA, and AZAMOSSADAT NOURBAKHSH, “MULTIMODAL BIOMETRIC RECOGNITION USING PARTICLE SWARM OPTIMIZATION-BASED SELECTED FEATURES,” JOURNAL OF INFORMATION SYSTEMS AND TELECOMMUNICATION (JIST), vol. 1, no. 2, pp. 79–87, 2013, [Online]. Available: https://sid.ir/paper/332636/en

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