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A Survey of Artificial Intelligence Techniques Used in Biometric Systems
Published in Chiranji Lal Chowdhary, Intelligent Systems, 2019
C. N. Patill, E. Naresh, B. P. Vijay Kumar, Prashanth Kambli
Iris recognition (Fig. 2.9) is one of the techniques of a biometric system that works with mathematical pattern recognition on images or video images of every individual. Complex pattern of iris recognition is unique, secure, stable, and fast access when it is compared with other modalities. Iris recognition system captures a high resolution image of an individual to maintain the quality of the image and also for the fast recognition purpose.8 The captured images are compared with the predefined images stored in the database. Here pattern recognition technology used to read and match the captured image and the image that is stored in the databases. There are mainly three steps in iris recognition for the identification, recognition, and authentication of an individual: live image capture, identifying the iris and optimizing the image, and comparing or matching the captured image with the stored images in the database.6,7
Iris Recognition
Published in Richard C. Dorf, Circuits, Signals, and Speech and Image Processing, 2018
Yingzi Du, Robert W. Ives, Delores M. Etter
Compared to other biometrics, such as face, fingerprint, and voice, the iris has a number of advantages. The iris is a highly protected, internal organ of the eye, and yet it is externally visible so patterns can be imaged from a distance. The iris has six times as many distinct, identifiable features as a fingerprint and a high information density (approximately 3.2 bits per square millimeter). Like fingerprints, iris patterns develop randomly. No two iris patterns are alike, even those of identical twins, and even between the right and left eye of the same person. The iris remains unchanged throughout a person’s life, although damage to the cornea, disease, or other ailments might hinder or prevent its use for identification. Iris recognition has the highest accuracy level of all biometrics, with a near 0% false accept rate (FAR) and very low false reject rate (FRR). It can provide fast, scalable authentication in large database environments.
Genetic Algorithm and BFOA-Based Iris and Palmprint Multimodal Biometric Digital Watermarking Models
Published in D. P. Acharjya, V. Santhi, Bio-Inspired Computing for Image and Video Processing, 2018
Iris recognition is an automated method of biometric identification that uses mathematical pattern-recognition techniques on video images of the irises of an individual’s eyes, whose complex random patterns are unique and can be seen from some distance. Many millions of individuals in several nations about the globe have been enrolled in iris recognition systems, for convenience purposes, such as passport-free automated border-crossings, and some national ID systems based on this technology are being deployed. A central advantage of iris recognition, besides its speed of matching and its extreme opposition to false matches, is the stableness of the iris as an inner, protected, yet externally visible organ of the optic system.
A New Design of Iris Recognition Using Hough Transform with K-Means Clustering and Enhanced Faster R-CNN
Published in Cybernetics and Systems, 2022
Gorla Babu, Abdul Khayum Pinjari
Iris recognition is a biometric identification and verification technique, which authenticates a human through an image of a person’s eye that is used for more accurate analysis because of the high entropic iris patterns (Liu et al. 2020). Iris is a circular membrane, which is located among the lens and the cornea of the human eye, which aims for controlling the amount of light crossing with the pupil by relaxing and contracting the dilator muscles and the pupillary sphincter (Labati et al. 2020). Due to the unique and invariant features of the human iris texture of each person, it is robust for using it in identification systems (Shuai et al. 2020). The major factors for developing an iris recognition model are the durability of the iris structure in the human’s lifetime, the unique pattern of the iris, and the user-friendly image attained by devices with a broad range of improved abilities that promotes biometric identification among the public (Proença and Neves 2018). Pattern recognition and computer vision sectors are the major efficient fields, which prevent diverse issues in multiple aspects. In recent years, with the progressions in technology particularly in biometrics, features have been promoted to get protection around the world (Trokielewicz, Czajka, and Maciejewicz 2020; Lin, Liu, and Chen 2009).
PClusBA: A Novel Partition Clustering-Based Biometric Authentication Mechanism
Published in IETE Journal of Research, 2022
Nageswari Amma N.G., Bhuvaneswari Amma N.G.
Various human physiological features, such as voice, iris, facial features, and fingerprints, are used in the process of biometric authentication. Voice/speech recognition involves recording human sound waves and reproducing the same for authentication. Obtaining undistorted sound waves from the source proves to be a challenge in this method. Iris recognition involves applying pattern recognition techniques in high-resolution images of the irises of an individual’s eyes. This method demands a very high-resolution image. Live tissue identification is difficult in iris recognition. Common facial features can also be used for biometric authentication [1]. Poor lighting and other facial changes deter this method from being efficient.