A New Human Identification Method: Sclera Recognition

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

doi:10.1109/tsmca.2011.2170416

Cited by 124 publications

(69 citation statements)

References 56 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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“…Zhou et al [15] used bank of Gabor filters with a line descriptor to create a binary blood vessels skeleton map. A discrete Meyer wavelet filter banks and Local Directional Pattern (LDP) were used in [16] for blood vessel enhancement and feature extraction.…”

Section: Literature Surveymentioning

confidence: 99%

“…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%

“…1, has a rich pattern of blood vessels which have different orientations and layers. Therefore, the discriminant characteristics of these blood vessels are thought a bright factor for eye recognition under visible wavelength illumination [3]. [3] In Table I we have shown the comparison of the different biometrics based on the following parameters: accuracy [4], stability [5], reliability [6], [7], identification (ID) and ID capability in a distance [8], user cooperation [6], and scalability to a large population [9].…”

Section: Introductionmentioning

confidence: 99%

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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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“…Recent Study ECG [13] PPG EEG Eye sclera [3] Acoustic gait recognition [16] Eye movements [17] 그림 5는 기존에 많이 연구되는 보행기반 사용자 인 식방식인 Motion capture 방식 [6] 과 Silhouettes 방식 [7] 그리고 최근에 새로 연구되는 Acoustic 방식 [17] 과 …”

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Human Identification using EMG Signal based Artificial Neural Network

김상호¹,

류재환²,

이병현³

et al. 2016

Journal of the Institute of Electronics and Information Enginee

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Recently, human identification using various biological signals has been studied and human identification based on the gait has been actively studied. In this paper, we propose a human identification based on the EMG(Electromyography) signal of the thigh muscles that are used when walking. Various features such as RMS, MAV, VAR, WAMP, ZC, SSC, IEMG, MMAV1, MMAV2, MAVSLP, SSI, WL are extracted from EMG signal data and ANN(Artificial Neural Network) classifier is used for human identification. When we evaluated the recognition ratio per channel and features to select approptiate channels and features for human identification. The experimental results show that the rectus femoris, semitendinous, vastus lateralis are appropriate muscles for human identification and MAV, ZC, IEMG, MMAV1, MAVSLP are adaptable features for human identification. Experimental results also show that the average recognition ratio of method of using all channels and features is 99.7% and that of using selected 3 channels and 5 features is 96%. Therefore, we confirm that the EMG signal can be applied to gait based human identification and EMG signal based human identification using small number of adaptive muscles and features shows good performance.

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A New Human Identification Method: Sclera Recognition

Cited by 124 publications

References 56 publications

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

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

Survey Paper on Sclera Segmentations Techniques

Human Identification using EMG Signal based Artificial Neural Network

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