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Abstract
The performance of an Automatic iris Identification System is impacted by both the poor quality of iris images and the uncertainty of information. Assessing image quality and rejecting poor-quality images can substantially improve the performances of the current biometric systems. The main idea behind our proposed Image Quality Assessment approaches is to take advantage, firstly, of the texture of iris images and, secondly, of the uncertainty of these information. This is achieved by defining a set of Contextual Quality Indicators extracted from the image texture and transforming them into Quality Assessment Criteria in the evidential framework, taking into account the information uncertainty degree. The Contextual Quality Indicators are defined based on a priori analysis of the context of the application. We use ‘iris’ as the context of application. Generally, only the normalized iris image is saved, i.e. the acquired iris image is not always available. So, the main advantage of our approaches over other related methods is that it can act in the normalization level of the processing chain to reject poor-quality images. So that, the subsequent Automatic iris Identification System can process only good-quality images, which result in better recognition rate performance. The functioning of our evidential approaches is illustrated using image samples from CASIA 1.0 database. The performance of over the proposed image quality assessment approaches is compared with the standard iris identification system without an image quality assessment step. A statistical test, based on 95% confidence interval, is used to assess if there is a statistically significant difference between the performances of the proposed approaches. The CASIA 1.0 has been used to make the comparison. The comparison results highlight the effectiveness of the proposed approaches for iris domain of applications. The source code of our paper is available at https://github.com/Sonda09/IIQA
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No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
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Notes on contributors
Amina Kchaou
Amina Kchaou was born in Tunisia in 1996. She received her Information and communication Technologies diploma in 2018 and the master's degree in Telecommunications and Network systems, in 2021, from the National School of Electronics and Telecommunications of Sfax - Tunisia. Now she is a Phd student in Science and Technologies of Information and Communication (STIC) in University of Sfax. She is a member of Laboratory of Signals, systeMs, aRtificial Intelligence, neTworkS(SM@RTS) in Sfax University. She works on information modelling and data and information quality.
Sonda Ammar Bouhamed
Dr. Sonda Ammar Bouhamed was born in Tunisia in 1986. She received her Informatique and Multimedia diploma in 2009, the master's degree in information processing system and Multimedia from Rouen University, Rouen, France, in 2010 and the Phd Degree in Computer Systems Engineering in 2014 from the National Engineering School of Sfax - Tunisia. Now she is an assistant professor in National School of Electronics and Telecommunication of Sfax (Enet-Com) and a member of Laboratory of Signals, systeMs, aRtificial Intelligence, neTworkS(SM@RTS) in Sfax University. She works on processing, analysis and fusion of information at various levels, and from various sensors, uncertain information modelling, data and information quality, small data analysis, feature selection and real time systems. Her research works are applied on obstacle detection and recognition. Since 2011 she manages a research team working on a smart white cane project.