Multi-angle sclera recognition system

Zhou, Zhi; Du, Eliza Yingzi; Thomas, N. Luke; Delp, Edward J.

doi:10.1109/cibim.2011.5949225

Cited by 33 publications

(23 citation statements)

References 27 publications

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“…[9] used the HSV color space with histogram equalization [10] used the same and low-pass filtering [7] in order to extract the sclera. For grayscale images, Otsu's thresholding method was applied by [11], [12], [13], [14], [15] to detect sclera regions as the intensity of the sclera area is different from the background. Among those algorithms presented by different researchers were thresholding, clustering (k-means), histogram based segmentation in combination with feature enhancement method came out the most used algorithm.…”

Section: Fig 1 Anatomy Of the Eyementioning

confidence: 99%

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

Hellwig¹

2018

IJRASET

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This paper presents a computationally efficient sclera detection, segmentation and enhancement approach. The proposed sclera detection and segmentation approach is developed based on the cellular automaton which evolves using the Grow-Cut algorithm and enhancement approach based on Multiscale Retinex. The major advantage of the developed approach is its computational simplicity, speed and accuracy as compared to the prior detection, segmentation and enhancement approaches developed for sclera segmentation images. The new used approach was tested in Intel i5 4570 with 3.2 GHZ processor and 16GB RAM and Intel Dual Core with 2.48GHZ processor and with 2GB RAM. Using these two different units, the result shows that segmentation under Intel i5 segment sclera for three 3 hours with an image size of 640x480 pixels while in Intel Dual Core 2GB RAM having the same image size segments the sclera for fourteen (14) hours and eighteen (18) minutes. The experimental results obtained from ND-IRIS 0405 database and publicly available images and respectively achieved better performance in terms of computational simplicity, speed and accuracy as compared to the previous proposed method. Multiscale Retinex enhanced image very fast with a highest minimum execution time of seventeen seconds (17secs) for an image size of 640 x 480 pixels for both colour and gray scale. The proposed approach for enhancement shows better performance in enhancing low contrast, dark with poor illumination images but colour rendition is quite poor for images having good illumination. The experimental results presented in this paper clearly demonstrate the applicability of the proposed sclera detection, segmentation and enhancement approach, i.e., significant reduction in computational complexity in detection segmentation and enhancement while providing comparable segmentation and enhancement performance.

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Section: Fig 1 Anatomy Of the Eyementioning

confidence: 99%

Interactive Growcut Multi-Label N-D Segmentation Based Algorithm for SCLERA Segmentation

Hellwig¹

2018

IJRASET

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“…In [31], a robust multi-angled sclera recognition technique was proposed. A new robust method of sclera segmentation for colour images was proposed in [32].…”

Section: Fig2 Typical Sclera Biometric Systemmentioning

confidence: 99%

“…[40] GLCM (Grey Level Cooccurrence Matrix) was used for sclera biometrics. The authors in [31] presented four fusion methods for combining recognition results from multi-angle images. LBP (Local Binary Pattern) feature was used for sclera biometrics in [41].…”

Section: Feature Extractionmentioning

confidence: 99%

“…For classification, template matching was used in [31]. In [32], [35] for classification, the Hamming distance was used for template-based matching.…”

Section: Classificationmentioning

confidence: 99%

“…In [32], [35] for classification, the Hamming distance was used for template-based matching. In [31] after feature extraction and matching technique SURF (Speeded Up Robust Features) was used for key point matching.…”

Section: Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Survey Paper on Sclera Segmentations Techniques

Rajole¹,

Shinde²

2016

International Journal of Advanced Research in Computer and Comm

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Due to extensive growth of internet, security of the internet is area of interest for the researchers. An efficient biometric trait these days are sclera blood vessels. Sclera is a white region in the eye, around the eyeball which contains blood vessel patterns that can be used for personal identification. In this paper we present the existing sclera segmentation methods and their working in with their limitations and ideas to enhance them.

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