Biometrics
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The rich texture of the iris can be used as a biometric cue for person recognition. This variability in Iris texture is due to the accumulation of multiple anatomical entities composing its structure. Even the left iris and the right iris of an individual exhibit significant differences in their texture, although some global similarities may be observed. Due to the presence of distinctive information at multiple scales a wavelet-based signal processing approach is commonly used to extract features from the iris
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OCT For Everyone..
OCT is a great technology,Many ophthalmologist find very difficult to understand it ,SO I have tired to simplify it as much as possible .Hope everyone can understand now onwards the basic about OCT .
Every feedback s most welcomed sothat i can improve further in coming days
Please email your feedback to me in the following address
yourgyanu@gmail.com
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Optical coherence tomography (OCT) is a non-invasive imaging technique that uses low-coherence interferometry to produce high-resolution, cross-sectional images of biological tissues. OCT works by measuring the backscattered light from internal tissue microstructures. The basic setup uses a Michelson interferometer with a broadband light source, such as a superluminescent diode, to capture back-reflected light. There are different types of OCT systems, including time domain OCT, frequency domain OCT, and swept-source OCT. OCT has various medical applications for imaging the eye, skin, and detecting cancer or other abnormalities in human cells.
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Final oct
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This document discusses various eye conditions and their treatments. It covers myopia, hyperopia, astigmatism and presbyopia. For presbyopia, it explains the age-related loss of elasticity in the lens and treatments like bifocal glasses. It also discusses cataracts, how they increase with age, and treating them by removing the lens and implanting an IOL. The document concludes with an overview of diabetic retinopathy, how it affects vision compared to normal eyes, and its effects over time.
OCT For Everyone..
OCT is a great technology,Many ophthalmologist find very difficult to understand it ,SO I have tired to simplify it as much as possible .Hope everyone can understand now onwards the basic about OCT .
Every feedback s most welcomed sothat i can improve further in coming days
Please email your feedback to me in the following address
yourgyanu@gmail.com
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Iris Recognition is a highly efficient biometric identification system with great possibilities for future in the
security systems area.Its robustness and unobtrusiveness, as opposed tomost of the currently deployed
systems, make it a good candidate to replace most of thesecurity systems around. By making use of the
distinctiveness of iris patterns, iris recognition systems obtain a unique mapping for each person.
Identification of this person is possible by applying appropriate matching algorithm.In this paper,
Daugman’s Rubber Sheet model is employed for irisnormalization and unwrapping, descriptive statistical
analysis of different feature detection operators is performed, features extracted is encoded using Haar
wavelets and for classification hammingdistance as a matching algorithm is used. The system was tested on
the UBIRIS database. The edge detection algorithm, Canny, is found to be the best one to extract most of
the iris texture. The success rate of feature detection using canny is 81%, False Accept Rate is 9% and
False Reject Rate is 10%.
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Biometrics is science of measuring and statistically analyzing biological data. Biometric system establishes identity of a person based on unique physical or behavioural characteristic possessed by an individual.Behavioural biometrics measures characteristics which are acquired naturally over time. Physical biometrics measures inherent physical characteristics on a n individual. Over the last few decades enormous attention is drawn towards ocular biometrics. Cues provided by ocular region have led to exploration of newer traits. Feasibility of periocular region as a useful biometric trait has been explored recently. With the promising results of preliminary examination, research towards periocular region is currently gaining lot of prominence. Researchers have analyzed various techniques of feature extraction and classification in the periocular region. The current paper investigates the effect of using Lower Central Periocular Region (LCPR) for identification. The results obtained are comparable with those acquired for the entire periocular region with an advantage of reduced periocular area
Iris recognition seminar
This document discusses iris recognition as a biometric method for uniquely identifying individuals. It begins by explaining biometrics and the need for identification methods due to advances in technology and globalization. It then describes the anatomy of the human eye and details how the iris is unique among individuals and stable over one's lifetime, making it suitable for recognition. The document explains John Daugman's algorithms for iris encoding and matching iris codes to identify individuals. It discusses applications of iris recognition including border control, ATM access, and forensic identification. The document concludes that iris recognition is a highly accurate and secure biometric method due to the statistical rarity of matching irises between individuals.
A Literature Review on Iris Segmentation Techniques for Iris Recognition Systems
This document reviews various techniques for iris segmentation in iris recognition systems. It discusses 8 techniques: (1) Integrodifferential operator, (2) Hough transform, (3) Masek method, (4) Fuzzy clustering algorithm, (5) Pulling and Pushing method, (6) Eight-neighbor connection based clustering, (7) Segmentation approach based on Fourier spectral density, and (8) Circular Gabor Filter. Each technique achieves some level of segmentation accuracy but also has disadvantages like high computational time, low accuracy, or poor performance on noisy images. The document concludes that a unified framework approach provides the highest overall segmentation accuracy for robustly segmenting iris images.
G01114650
This document reviews various techniques for iris segmentation in iris recognition systems. It discusses integrodifferential operator and Hough transform approaches, as well as the Masek, fuzzy clustering, and pulling and pushing methods. Each approach has advantages and disadvantages. The Masek method achieves circular iris and pupil localization but has lower accuracy and speed. Fuzzy clustering provides better segmentation for non-cooperative iris recognition but requires an extensive search. The pulling and pushing method aims to develop a more accurate and rapid iris segmentation algorithm.
Iris recognition system
The document discusses iris recognition as a biometric identification method. It provides a brief history of iris recognition from its proposal in 1939 to its implementation in 1990 by Dr. John Daugman who created algorithms for it. The document outlines the iris recognition process including iris localization, normalization, feature extraction using Gabor filters, and matching using techniques like Euclidean distance. It discusses advantages like accuracy and stability of iris patterns, and disadvantages such as cost and inability to capture images from certain positions.
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EFFECTIVENESS OF FEATURE DETECTION OPERATORS ON THE PERFORMANCE OF IRIS BIOME...
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International Journal of Engineering and Science Invention (IJESI)
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Biometrics
- 1. Unit-I Biometrics Iris, Speech etc. Presented by: Tabassum Jahan
- 2. Anatomy of Human Eye
- 3. Iris Recognition : Introduction The ocular region of the human face consists of the eyes and the surrounding structures such as facial skin, eyebrows, and nose. While various components of the eye have been proposed as biometric indicators (like, iris, retina etc.), it is the iris that has been extensively studied in the biometrics literature and used in large-scale biometric systems. The iris is an internal organ of the eye that is located just behind the cornea and in front of the lens. The iris is the colored structure located in the annular region of the eye and is bounded by The pupil (the dark opening in the eye) The sclera (the white of the eye). The primary function of the iris is to regulate the amount of light entering the eye by dilating or contracting a small opening in it called the pupil.
- 4. Anatomy of the iris as observed by a near-infrared camera placed in front of the eye. The rich texture of the iris is due to its anterior portion consisting of the musculature, stroma, and the anterior border layers. When the anterior border layer recedes, the collarette becomes visible as a zig-zag line separating the ciliary zone from the pupillary zone. Crater-like irregular structures called crypts are often observed when the anterior layer thins out, thereby revealing the heavily pigmented and much darker posterior layer.
- 5. Iris Recognition : Introduction contd.. The rich texture of the iris can be used as a biometric cue for person recognition. This variability in Iris texture is due to the accumulation of multiple anatomical entities composing its structure. Even the left iris and the right iris of an individual exhibit significant differences in their texture, although some global similarities may be observed. Due to the presence of distinctive information at multiple scales a wavelet-based signal processing approach is commonly used to extract features from the iris. Typical iris recognition system does not use the color of the iris for human recognition For Matching approaches like Hamming distance are used to compare two iris codes.
- 6. Design of an Iris Recognition System Note The performance (matching) of an iris recognition system is impacted by the accuracy of the segmentation module
- 7. Iris Recognition : Steps Acquisition to obtain a 2D image of the eye typically using NIR electromagnetic spectrum sensitive sensitive CCD camera Segmentation localizes the spatial extent of the iris Normalization a geometric normalization scheme to transform the iris texture within the annular region from cartesian coordinates to pseudo polar coordinates (“unwrapping of the iris” ) Encoding : feature extraction routine is used to encode its textural content. Iris Code Matching optionally after aligning the two iris codes, compared using the Hamming distance.
- 8. Iris Recognition Challenge Acquisition Issues Moving Eye, Head rotation Motion blur, Non-uniform Illumination Out-of-focus imaging, Occlusion from eyelids and eyelashes, Off-axis gaze or camera angle, Poor contrast, resolution Reflection and natural luminosity Blurred Dilation Printed Contact Lenses Segmentation issues Transmission noise Security and Privacy Issues