a survey on multi-biometric fusion approaches

Ayash, Hawraa Abed Alkareem Hussain; Abbas, Hawraa Hassan

doi:10.63463/kjes1074

	a survey on multi-biometric fusion approaches
Kerbala Journal for Engineering Sciences (KJES)
Volume 3, Issue 2, June 2023, Pages 79-100 PDF (1.35 M)
Document Type: Review Article
DOI: 10.63463/kjes1074
Authors
hawraa Abed alkareem hussain Ayash^* ¹; hawraa hassan abbas²
¹Computer science.babylon university .
²Department of Electrical & Electronic Engineering ,College of Engineering, University of kerbala, kerbala, iraq.
Abstract
The goal of biometrics is to reliably and robustly identify people based on their unique personal characteristics, primarily for security and authentication needs, but also to identify and track the users of more intelligent applications. Fingerprints, iris, palm print, face and voices are frequently used modalities, but there are numerous more potential biometrics, such as stride, ear image, retina, DNA, and even behavior. As an automatic way to identify persons depend on just one (single modal biometrics) or a mix of (multi-modal biometrics). A fusion of two or more photos can be utilized to create multimodal biometrics, and the resulting fused image will be more secure. Several levels of fusion techniques are currently accessible, including feature level, decision level, matching score level, etc. In order to identify the human biometric by extracting features and classifying images, this paper discusses different fusion approaches that are implemented in multimodal biometrics. It also describes the datasets that were used and the outcomes ,results and conclusions that were obtained. The goal of biometrics is to reliably and robustly identify people based on their unique personal characteristics, primarily for security and authentication needs, but also to identify and track the users of more intelligent applications. Fingerprints, iris, palm print, face and voices are frequently used modalities, but there are numerous more potential biometrics, such as stride, ear image, retina, DNA, and even behavior. As an automatic way to identify persons depend on just one (single modal biometrics) or a mix of (multi-modal biometrics). A fusion of two or more photos can be utilized to create multimodal biometrics, and the resulting fused image will be more secure. Several levels of fusion techniques are currently accessible, including feature level, decision level, matching score level, etc. In order to identify the human biometric by extracting features and classifying images, this paper discusses different fusion approaches that are implemented in multimodal biometrics. It also describes the datasets that were used and the outcomes ,results and conclusions that were obtained.
Keywords
biometric fusion; Deep Learning; Convolutional Neural Networks; recognition system; multimodal biometric system

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