2018
DOI: 10.1007/s00521-018-3805-6
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RETRACTED ARTICLE: Ear recognition system using adaptive approach Runge–Kutta (AARK) threshold segmentation with ANFIS classification

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Cited by 13 publications
(3 citation statements)
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“…Ear recognition methods can be partitioned into three, Statistics, Sparse Representation, and Neural Networks. In order to recognize ears, Kondappan et al [2] used neural networks. The authors created a multi-layer feedforward network with unweighted linkages between nodes by combining ANN with fuzzy classifiers.…”
Section: E Recognitionmentioning
confidence: 99%
See 1 more Smart Citation
“…Ear recognition methods can be partitioned into three, Statistics, Sparse Representation, and Neural Networks. In order to recognize ears, Kondappan et al [2] used neural networks. The authors created a multi-layer feedforward network with unweighted linkages between nodes by combining ANN with fuzzy classifiers.…”
Section: E Recognitionmentioning
confidence: 99%
“…On the other hand, ear recognition is a sort of new biometrics, the hypothesis and examination of ear recognition excite additional consideration from homegrown and unfamiliar researchers. Different specialists have confirmed that the ears are for sure exceptional enough to recognize an individual and they could have practical use as biometric features [2].…”
Section: Introductionmentioning
confidence: 99%
“…ousands of kinds of image segmentation algorithms have been proposed, and new image segmentation algorithms are constantly being born. Common image segmentation methods include threshold-based segmentation methods, edge-based segmentation methods, and region-based segmentation methods [8][9][10][11][12][13]. In recent years, Bo et al [14] proposed a new deformable contour model for ultrasonic image sequence segmentation, which can resist the in uence of misleading or weak boundary in ultrasonic image segmentation.…”
Section: Introductionmentioning
confidence: 99%