Iris Recognition Using Fuzzy Min-Max Neural Network

Essentially use iris technique to human identification and recognition. Due enormous growth through the recent years for iris recognition techniques numerous research has emerged in the areas like; image compression, segmentation, quality assessment, Image Acquisition (IA), restoration, feature extraction (FE), normalization, noise reduction iris code matching (NRCM), evaluation, searching large database, applications, performance under changing multibiometrics and condition. Where many techniques were suggested to iris recognition (IR) using neural network because their efficient algorithm and also gives good result. In this research paper, we provide reviews a background for many techniques proposed to recognition iris image in various domains and also procedure a comparison between these techniques.

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“…In this paper, S.S. Chowhan and G. N. Shinde [5]. performance iris recognition and classification system.…”

Section: Introductionmentioning

confidence: 99%

Subject Review: Iris Recognition Using Neural Network

Chyad¹,

Mustafa²,

Haleot

2019

IJERAT

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“…The parallel hardware can be used to accelerate the execution as implementation can be done using single precision arithmetic operations. Researchers have used this classifier for several applications such as medical diagnosis [19], fault diagnosis [20], detection of heart diseases [21], face recognition [22], speaker identification [23], Iris recognition [24], business intelligence [25], outlier detection [26][27][28], etc. The FMN is a three layer feed-forward neural network model that grows adaptively to meet the demands of a given problem.…”

Section: Introductionmentioning

confidence: 99%

Optimized Fuzzy Min-Max Neural Network: An Efficient Approach for Supervised Outlier Detection

Upasani

2018

NNW

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Fuzzy min-max neural network (FMN), proposed by Simpson is a wellknown supervised neuro-fuzzy classifier that has been successfully used by many researchers for pattern recognition. However, the FMN represents the learned knowledge with exhaustive details in a 'fine-grained' manner that reduces its performance for pattern recognition in terms of the recall time per pattern. In this paper, we adapt the basic architecture of the FMN to represent the learned knowledge in a compact way that is in a 'coarse-grained' manner, which is closed to human thinking. The working of the proposed method that is fuzzy min-max neural network with knowledge compaction (FMN-KC) is illustrated using the Fisher Iris dataset. The potential of using the FMN-KC for supervised outlier detection is demonstrated using a time-series disk defect dataset published by NASA and KDD cup 99 dataset available in UCI repository. The proposed method achieves around 50 % gain in the recall time as compared to the original FMN and the recognition rate is also comparable. We strongly recommend using the proposed architecture FMN-KC for supervised outlier detection in the real time applications, where recall time per pattern is one of the key parameters.

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“…Fuzzy logic system using linguistic information can model the qualitative aspects of human knowledge and reasoning processess without much quanititative analysis [8]. But the fuzzy systems lack the capability of learning and have no memory [9].…”

Section: Introductionmentioning

confidence: 99%

Modified fuzzy hyperline-segment neural network for classification with mixed attribues

Shinde¹,

Kulkarni

2014

Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

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The fuzzy hyperline segment neural network (FHLSNN) utilizes fuzzy sets as pattern classes in which each fuzzy set is an union of fuzzy set hyperline segments. The fuzzy set hyperline segment is a n-dimensional hyperline segment defined by two end points with a corresponding membership function. In FHLSNN, membership function calculates membership value of the input pattern based on its distance from both the end points of the hyperline segment. But sometimes input pattern is nearer to the hyperline segment but far from its endpoints. To solve this problem, this paper proposes modified fuzzy hyperline segment neural network (MFHLSNN). In MHLSNN membership function is based on minimum of the distance of the input pattern from the midpoint of the hyperline segment and its distance from both the end points. The proposed model is applied to eight different benchmark datasets taken from the UCI machine learning repository. The experimental results of the MFHLSNN are compared with earlier methods like fuzzy min-max neural network, generalized fuzzy min-max neural network and fuzzy hyperline segment neural network. These results show that the MFHLSNN gives improved performance as compared to its earlier methods.

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Iris Recognition Using Fuzzy Min-Max Neural Network

Cited by 15 publications

References 7 publications

Subject Review: Iris Recognition Using Neural Network

Subject Review: Iris Recognition Using Neural Network

Optimized Fuzzy Min-Max Neural Network: An Efficient Approach for Supervised Outlier Detection

Modified fuzzy hyperline-segment neural network for classification with mixed attribues

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