An Improved Low Contrast Image in Normalization Process for Iris Recognition System

Ghali, Abdulrahman Aminu; Jamel, Sapiee; Mohamad, Kamaruddin Malik; Khalid, Shamsul Kamal Ahmad; Pindar, Zahraddeen Abubakar; Deris, Mustafa Mat

doi:10.1007/978-3-319-72550-5_47

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…is function was intended to change the grey levels to increase the image contrast and highlight the iris texture (Figure 9(c)) that represented the relevant characteristics of the human eye [30].…”

Section: Iris Normalisation and Enhancementmentioning

confidence: 99%

“…Claudio et al [12] successfully detected the iris in its coronal axis in order to remedy errors of iris location in different positions. Other works mainly remedied problems of poor light distribution and focused on contrast correction [13,30]. We concentrated on carrying out an approach that guides the user to the correct head position during the acquisition phase in order to refine the stage of signature extraction.…”

Section: Introductionmentioning

confidence: 99%

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Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

Akrout

Fakhfakh

2020

Mathematical Problems in Engineering

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Current research in biometrics aims to develop high-performance tools, which would make it possible to better extract the traits specific to each individual and to grasp their discriminating characteristics. This research is based on high-level analyses of images, captured from the candidate to identify, for a better understanding and interpretation of these signals. Several biometric identification systems exist. The recognition systems based on the iris have many advantages and they are among the most reliable. In this paper, we propose a new approach based on biometric iris authentication. A new scheme was made in this work that consists of calculating a three-dimensional head pose to capture a good iris image from a video sequence which affects the identification results. From this image, we were able to locate the iris and analyse its texture by intelligent use of Meyer wavelets. Our approach was evaluated and approved through two databases CASIA Iris Distance and MiraclHB. The comparative study showed its effectiveness compared to those in the literature.

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Section: Iris Normalisation and Enhancementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

Akrout

Fakhfakh

2020

Mathematical Problems in Engineering

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A Brief Research and Lookback of the Development of Iris Recognition

et al. 2019

2019 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI)

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Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review

2022

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A system’s operational life cycle now includes an integrated health management and diagnostic strategy due to improvements in the current technology. It is evident that the life cycle may be used to identify abnormalities, analyze failures, and forecast future conditions based on current data. Data models can be trained using machine learning and statistical ideas, employing condition data and on-site feedback. Once data models are trained, the data-processing logic can be integrated into onboard controllers, allowing for real-time health evaluation and analysis. Interestingly, the oil and gas industries may encounter numerous obstacles and hurdles as a result of the integration, highlighting the need for creative solutions to the perplexing problem. The potential benefits in terms of challenges involving feature extraction and data classification, machine learning has received significant research attention recently. The application and utility in pump system health management should be investigated to explore the extend it can be used to increase overall system resilience or identify potential financial advantages for maintenance, repair, and overhaul activities. This is seen as an evolving research area, with a variety of application domains. This article present a critical analysis of machine learning’s most current advances in the field of artificial intelligence-based system health management, specifically in terms of pump applications in the oil and gas industries. To further understand its potential, various algorithms and related theories are examined. Based on the examined studies, machine learning shows potential for prognostics and defect diagnosis. There are, few drawbacks that is seen to be preventing its widespread adoption which prompt for further improvement. The article discussed possible solutions to the identified drawbacks and future opportunities presented. This study further elaborates on the commonly available commercial machine learning (ML) tools used for pump fault prognostics and diagnostics with an emphasis on the type of data utilized. Findings from the literature review shows that the neural network (NN) is the most prevalent algorithm employed in studies, followed by the Bayesian network (BN), support vector machine (SVM), and hybrid models. While the need for selecting appropriate training algorithms is seen to be significant. Interestingly, no specific method or algorithm exists for a given problem instead the solution relies on the type of data and the algorithm’s or method’s aptitude for resolving the provided errors. Among the various research studies on pump fault diagnosis and prognosis, the most frequently discussed problem is a bearing fault, with a percentage of 46%, followed by cavitation. The studies rank seal damage as the third most prevalent flaw. Leakage and obstruction are the least studied defects in research. The main data types used in machine learning techniques for diagnosing pump faults are vibration and flow, which might not be sufficient to identify the condition of pumps and their characteristics. The various datasets have been derived from expert opinion, real-world observations, laboratory tests, and computer simulations. Field data have frequently been used to create experimental datasets and simulated data. In comparison to the algorithmic approach, the data approach has not received significant research attention.

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An Improved Low Contrast Image in Normalization Process for Iris Recognition System

Cited by 3 publications

References 14 publications

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

Three-Dimensional Head-Pose Estimation for Smart Iris Recognition from a Calibrated Camera

A Brief Research and Lookback of the Development of Iris Recognition

Prognostic Health Management of Pumps Using Artificial Intelligence in the Oil and Gas Sector: A Review

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