SIFT based iris recognition with normalization and enhancement

Yang, Gongping; Pang, Shaohua; Yin, Yilong; Li, Yanan; Li, Xuzhou

doi:10.1007/s13042-012-0101-0

Cited by 18 publications

(6 citation statements)

References 18 publications

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“…many features are extracted to create the feature vector of the iris. The 2D Gabor wavelets [2], SIFT [9], DCT (Discrete Cosine Transform) [10], Discrete Fourier Transform [11], Independent Component Analysis [12], Principal Component Analysis [13].…”

Section: Related Workmentioning

confidence: 99%

Human Identification Using Multi region PCA for Iris Recognition

Lefkovits¹,

Crisan²,

Emerich³

et al. 2017

Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017

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Abstract-Biometric identification is a constantly growing field. Many physiological characteristics may be used for reliable identification. The most secure among them uses the iris pattern, because it is unique and stable from eye to eye and from person to person. The approach presented in this article guides through every step of iris-based human identification system from eye localization to iris extraction and identification using PCA features. The novelty of this paper consists of the application of PCA on multi-region iris images. The PCA identification based on multiple regions of interest (ROI) allows higher identification accuracy while extracting much fewer features.

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Section: Related Workmentioning

confidence: 99%

Human Identification Using Multi region PCA for Iris Recognition

Lefkovits¹,

Crisan²,

Emerich³

et al. 2017

Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017

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“…The method is scale invariant and doesn't require affine transformation or highly accurate segmentation. Yang et al [21] showed that normalization and enhancement are crucial for SIFT-based iris recognition. In [22], to better extract the 978-1-4799-7626-3/15/$31.00 ©2015 IEEE iris features, the Gabor wavelet was used in a new way for local feature point description using SIFT.…”

Section: Related Workmentioning

confidence: 99%

Efficient enhancement and matching for iris recognition using SURF

Ismail

Ali

Farag

2015

2015 5th National Symposium on Information Technology: Towards New Smart World (NSITNSW)

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Iris recognition is gaining more attention and the development of the field is increasing rapidly. This paper presents a complete iris recognition system. The iris features are obtained using Speeded Up Robust Features (SURF) after enhancing the image using Contrast Limited Adaptive Histogram Equalization (CLAHE). A novel matching algorithm based on applying fusion rules at different levels is proposed. The algorithm has the advantage of reduced data storage and fast matching. It can also handle efficiently the problem of rotation, scaling, illumination variation and occlusions. The proposed algorithm is implemented and tested using CASIA (V4) database. The recognition accuracies obtained are 99% using left images and 99.5% using right images. Results show that fusion of right and left images scores increases the recognition accuracy. The recognition accuracies obtained after fusion are 99.5% and 100% using minimum and sum rules respectively. Moreover, the proposed algorithm has an excellent robustness with respect to increasing the number of subjects.

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“…Tables 3 and 4 shows GAR, FAR, FRR, EER and TER for the proposed method with and without normalization respectively. Boles et al [11] 24.68 Patel et al [22] 5.14 Yang et al [23] 3.01 Proposed method without normalization 7.03 Proposed method with normalization 2.21…”

Section: Ter ¼ Far þ Frrmentioning

confidence: 99%

Partial Segmentation and Matching Technique for Iris Recognition

Deshmukh

Prasad

2014

Computational Intelligence in Data Mining - Volume 1

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One of the main issues in iris segmentation is that the upper and lower region of the iris is occluded by the eyelashes and eyelids. In this paper, an effort has been made to solve the above problem. The pupil boundary is detected using adaptive thresholding and circular hough transform (CHT). The iris boundary is detected by drawing arcs of different radius from the pupil center and finding maximum change in intensity. The annular region between the iris inner and outer boundary is partially segmented. The segmented iris is transformed into adaptive rectangular size strip. Features are extracted using scale-invariant feature transform (SIFT). The experimental results show that the proposed technique has achieved high accuracy and low error rate.

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SIFT based iris recognition with normalization and enhancement

Cited by 18 publications

References 18 publications

Human Identification Using Multi region PCA for Iris Recognition

Human Identification Using Multi region PCA for Iris Recognition

Efficient enhancement and matching for iris recognition using SURF

Partial Segmentation and Matching Technique for Iris Recognition

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