Key Takeaways from Iris Recognition Algorithms Review:

  • Iris recognition is a reliable method for personal identification in biometric systems, commonly used in commercial and government applications [1].
  • Commonly used algorithms in iris recognition stages include the Hough transform, rubber sheet model, wavelet, Gabor filter, and hamming distance, showcasing their potential to enhance the iris recognition system [1].
  • The demodulation of patterns is done using quadrature 2D Gabor wavelets, and the Hamming distance measures unlikeness between binary templates in iris recognition¬†[2].
  • Fusion of iris recognition features can be done using a weighted sum rule based on performance measures, showing competitive results compared to state-of-the-art algorithms¬†[3].

Sources:

[1] (PDF) A Review of Iris Recognition Algorithms
With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays. The system has been used for years in many commercial and government applications that allow access control in places such as office, laboratory, armoury, automated teller machines (ATMs), and border control in airport. The aim of the paper is to review iris recognition algorithms. Iris recognition system consists of four main stages which are segmentation, normalization, feature extraction and matching. Based on the findings, the Hough transform, rubber sheet model, wavelet, Gabor filter, and hamming distance are the most common used algorithms in iris recognition stages. This shows that, the algorithms have the potential and capability to enhanced iris recognition system. Figures – available via license: CC BY-SAContent may be subject to copyright. Discover the world’s research25 million members160 million publication pages2.3 billion citationsJoin for free 175 A Review of Iris Recogntion Algorithms Abdulrahman Aminu Ghali, Sapiee Jamel, Kamaruddin Malik Mohamad, Nasir Abubakar Yakub, Mustafa Mat Deris Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia E-mail:aminuabdulrahman81yahoo.com, sapieeuthm.edu.my, malikuthm.edu.my, aynasirgmail.com, mmustafauthm.edu.my Abstract– With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays.

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[2]1737-1740, 2001. … Demodulation of every pattern is done to obtain phase information through quadrature 2D Gabor wavelets. 2D Gabor filter is used in the Extraction of iris features [111], [112]. To eliminate the DC components induced by bright backgrounds, the log gabor filter is Gaussian on a logarithmic scale and features a stringent bandpass filter that permits a certain band of frequencies while rejecting the rest. …… Hamming distance is a fraction of unlikeness between two binary templates [119], [112].

[3]The fusion is carried out using a proposed weighted sum rule relies on the ranking of three performance measures. The proposed fusion rule computes weights, which represent the reliability degree to which each individual source must contribute in order to determine the more discriminative matching scores. Our experiments rely on iris standard databases which as a whole constitute a challenging and perfect example of variable image quality conditions. According to the results, our proposal is very competitive and outperforms the state-of-the-art algorithms on the topic. In addition, it is demonstrated that the proposed keypoints-based feature extraction method is feasible and that it could be used even in real-time applications if the database is previously processed.In this paper, we present the evolution of the open source iris recognition system OSIRIS through its more relevant versions: OSIRISV2, OSIRISV4, and OSIRISV4.1.

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