Efficient enhancement and matching for iris recognition using SURF

Ismail, Asmaa I.; Ali, Hanaa S.; Farag, Fathi A.

doi:10.1109/nsitnsw.2015.7176409

Cited by 12 publications

(5 citation statements)

References 30 publications

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“…The workflow of most traditional iris recognition systems [3][4][5][6][7][8][9][10][11][12] can be split into the following steps: iris localization, segmentation, normalization, feature enhancement, feature extraction, and identification. To increase system performance, evaluation index estimation and more complex methods were proposed.…”

Section: A Iris Recognition Systemmentioning

confidence: 99%

“…Common techniques such as the Gabor filter, histogram equalization, and Contrast Limited Adaptive Histogram Equalization (CLAHE) are widely applied to enhance iris features. The Gabor filter was adapted to extract specific frequency information in the images and is therefore suitable for texture extraction, and CLAHE was employed to enhance image contrast in [10]. Feature extraction algorithms of iris recognition systems include Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The Gabor filter was adapted to extract specific frequency information in the images and is therefore suitable for texture extraction, and CLAHE was employed to enhance image contrast in [10]. Feature extraction algorithms of iris recognition systems include Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) [10][11][12]. SURF is a modified version of SIFT and is several times faster than SIFT.…”

Section: Introductionmentioning

confidence: 99%

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Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Hsiao,

Chang,

Fan

2024

JAIT

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In this study, the proposed iris recognition method uses the You Only Look Once (YOLO)-based deep learning algorithm with the procedure divided into two stages. After extraction of the iris and pupil from the images, the iris Region of Interest (ROI) is identified by the classifier. Iris localization, iris segmentation, and feature enhancement are three crucial processes when extracting the iris ROI, and they constitute the first stage. Iris localization is firstly discussed, and the three methods are proposed with the system performance analyzed from the perspective of both system safety and affordability. The main difference among these methods is their complexity. Iris segmentation is then introduced, and an experiment is conducted to evaluate system performance when images are preprocessed for inputs by different segmentation methods, including images with and without normalization. Normalization and its necessary or unnecessary role in identifying images with deep learning are then analyzed. Finally, an examination of how feature enhancement influences the results of the proposed method is outlined. For system safety analysis, the Equal Error Rate (EER) of the proposed design approaches near zero; for system affordability analysis, the accuracy of the proposed design can be up to 98%.

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Section: A Iris Recognition Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Hsiao,

Chang,

Fan

2024

JAIT

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show abstract

“…For segmentation process, both Canny edge detection operator and circular Hough transformation were applied [9]. For normalization, the Daugman's rubber sheet model [14] was used where the Iris disk was converted to a rectangular region with prefixed size. The image was enhanced by using the Contrast Limited Adaptive Histogram Equalization CLAHE algorithm [23] [14].…”

Section: Iris Image Pre-processingmentioning

confidence: 99%

Efficient Classifying and Indexing for Large Iris Database Based on Enhanced Clustering Method

Khalaf¹,

Mohammed²,

Moorthy³

et al. 2019

STUD INFORM CONTROL

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Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. A new method is presented in this paper to extract the most relevant features of iris biometric images for indexing the iris database. Three transformation methods DCT, DWT and SVD were used to analyse the iris image and to extract its local features. The clustering method shouldering on the responsibility of determining the partitioning and classification efficiencies of the system has been improved. In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. The Penetration Rates underwent reductions and reached the levels of 0.98, 0.13 and 0.12 for three different databases. Also, the Bin Miss Rates decreased to 0.3037, 0.4226 and 0.2019 for the investigated databases.

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“…Iris-based recognition and verification [7,11,12], on the other hand, is done by applying segmentation in the region of the iris to extract its radius. is is followed by feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method

Khan

Lee

Khan

et al. 2020

Scientific Programming

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Fingerprint registration and verification is an active area of research in the field of image processing. Usually, fingerprints are obtained from sensors; however, there is recent interest in using images of fingers obtained from digital cameras instead of scanners. An unaddressed issue in the processing of fingerprints extracted from digital images is the angle of the finger during image capture. To match a fingerprint with 100% accuracy, the angles of the matching features should be similar. This paper proposes a rotation and scale-invariant decision-making method for the intelligent registration and recognition of fingerprints. A digital image of a finger is taken as the input and compared with a reference image for derotation. Derotation is performed by applying binary segmentation on both images, followed by the application of speeded up robust feature (SURF) extraction and then feature matching. Potential inliers are extracted from matched features by applying the M-estimator. Matched inlier points are used to form a homography matrix, the difference in the rotation angles of the finger in both the input and reference images is calculated, and finally, derotation is performed. Input fingerprint features are extracted and compared or stored based on the decision support system required for the situation.

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Efficient enhancement and matching for iris recognition using SURF

Cited by 12 publications

References 30 publications

Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Efficient Classifying and Indexing for Large Iris Database Based on Enhanced Clustering Method

A Digital Camera-Based Rotation-Invariant Fingerprint Verification Method

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