Iris Recognition with Taylor Expansion Features

Bastys, Algirdas; Kranauskas, Justas; Krüger, Volker

doi:10.1007/978-1-4471-6784-6_8

Cited by 1 publication

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The iris contains a complex texture of visible particles of different sizes [2]. In the biological field, these particles are known as the features of the iris, which contains freckles, furrows, stripes, and coronas [2,18]. The existence of these particles is unique from one eye to another.…”

Section: Iris Featurementioning

confidence: 99%

“…The existence of these particles is unique from one eye to another. Previously, there have been different approaches proposed to detect the particles on the iris surface, ranging from phase-based techniques, techniques based on zero-crossings, and texture-based iris descriptors [2].…”

Section: Iris Featurementioning

confidence: 99%

“…On the other hand, from the biometric perspective, there are other types of iris features typically used for identity recognition purposes. The prominent features are known as vector, binary, boundary, and edge features [1,2,5,10]. In the past, there have been approaches to extract the features in order to segment the iris.…”

Section: Iris Featurementioning

confidence: 99%

“…In the past, there have been approaches to extract the features in order to segment the iris. The common approaches are Integro-Differential Operator algorithm, gradient-based edge detector, wavelet analysis, and Gabor filter [1,2,5,10]. This study will use colour, instead of the prominent features, in order to detect the pigment spots on the iris surface.…”

Section: Iris Featurementioning

confidence: 99%

“…Once the iris image is determined for this study, the next step is to look into the prominent feature detection approaches that has been widely used and proposed by other researchers in iris biometric field. Daugman, the pioneer of the automatic iris recognition system had used an Integro-Differential Operator algorithm to detect the boundary feature from the iris image [1,2,5,10]. Then, Wildes et.…”

Section: Colour Feature Extraction Approachmentioning

confidence: 99%

See 4 more Smart Citations

Iris Pigment Spots Detection implementing Thresholding Method

Man,

Jabal,

Rahim

et al. 2018

IJET

View full text Add to dashboard Cite

The increment size of the pigment spots on iris surface is indicated to the eye disease. Therefore, an automatic pigment spot detection has been proposed to detect the pigment spots on the iris surface. The main challenge is the type of feature that needs to be used for detection is unidentified. Based on the standard features applied for detection purposes, most of the features, such as shape, edges and vector, are not reliable. This situation occurs because the physical form of the pigment spots on the iris surface are dynamic. Hence, the pigment spots colour is the best feature possible to be applied, because it is moderately consistent. However, the spot colour intensity value are numerous. Several colour intensity values that have been used by other researchers were unable to detect the pigment spots. Henceforth, new colour intensity values based on thresholding method have been proposed in this paper. The approach has been applied through on the HSV colour model. The result shows the proposed values more accurate to detect the spots on the iris surface. The results have been recorded as follows (FAR) 0%, 1.33% and 4%, (FRR) 80%, 73.33%, and 70.67%, (DR) 20%, 25.33% and 25.33%.

show abstract

Section: Iris Featurementioning

confidence: 99%

Section: Iris Featurementioning

confidence: 99%