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Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual's eyes, whose complex patterns are unique, stable, and can be seen from some distance.
Mar 24, 2018 · “Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of one or both of the irises of an individual’s eyes, whose complex patterns are unique, stable, and can be seen from some distance.”
Aug 23, 2021 · Among all biometric recognition systems, the iris recognition system (IRS) is the system with higher efficiency and is the more reliable system for checking authenticity [ 9, 10 ].
iris recognition system. In subject area: Computer Science. An iris recognition system is a biometric technology that involves acquiring, segmenting, normalizing, extracting features, and matching iris images to verify or identify individuals with high accuracy and security.
The main objective of this comparative study is to present a comprehensive review of literature on one of the biometric identification systems namely the iris recognition system. Biometric authentication has been introduced as one of the most fundamental security technologies.
The procedures for iris recognition usually consist of four stages: image acquisition, iris segmentation, feature extraction, and pattern matching. The iris recognition has been acknowledged as one of the most accurate biometric modalities because of its high recognition rate.
Mar 5, 2024 · In terms of biometrics technologies, it’s not surprising that iris recognition has also seen an increasing adoption of purely data-driven approaches at all stages of the recognition pipeline: from preprocessing (such as off-axis gaze correction), segmentation, and encoding to matching.
Biometrics is considered as the use of physiological and behavior characteristics to decide an individual. Numerous biometric attributes have been enhanced and.
The recognition principle is the failure of a test of statis-tical independence on iris phase structure encoded by multi-scale quadrature wavelets.
Garg et al present an iris recognition system utilizing 2DPCA for feature extraction and a Genetic Algorithm (GA) to select features to reduce dimensionality without losing critical information. Using Levenberg–Marquardt's learning rule and a BPNN, the system achieves a noteworthy 96.40% classification accuracy. The technique, tested on the CASIA iris picture database, demonstrates an effective method to improve the functionality of iris recognition systems and provides secure human ...
