Neural network based biometric personal identification with fast iris segmentation

Abiyev, Rahib H.; Altunkaya, Koray

doi:10.1007/s12555-009-0103-1

Cited by 24 publications

(7 citation statements)

References 23 publications

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“…Backpropagation neural network is based on a supervised learning algorithm, and they are very important and useful in pattern recognition problems [ 17 , 19 , 35 ]. The training of backpropagation networks includes the update of parameters in order to produce good classification results.…”

Section: Simulationsmentioning

confidence: 99%

Deep Convolutional Neural Networks for Chest Diseases Detection

Abiyev

Ma’aitah

2018

Journal of Healthcare Engineering

264

118

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Chest diseases are very serious health problems in the life of people. These diseases include chronic obstructive pulmonary disease, pneumonia, asthma, tuberculosis, and lung diseases. The timely diagnosis of chest diseases is very important. Many methods have been developed for this purpose. In this paper, we demonstrate the feasibility of classifying the chest pathologies in chest X-rays using conventional and deep learning approaches. In the paper, convolutional neural networks (CNNs) are presented for the diagnosis of chest diseases. The architecture of CNN and its design principle are presented. For comparative purpose, backpropagation neural networks (BPNNs) with supervised learning, competitive neural networks (CpNNs) with unsupervised learning are also constructed for diagnosis chest diseases. All the considered networks CNN, BPNN, and CpNN are trained and tested on the same chest X-ray database, and the performance of each network is discussed. Comparative results in terms of accuracy, error rate, and training time between the networks are presented.

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Section: Simulationsmentioning

confidence: 99%

Deep Convolutional Neural Networks for Chest Diseases Detection

Abiyev

Ma’aitah

2018

Journal of Healthcare Engineering

264

118

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“…Daugman [8] developed 2D gabor filtration to encrypt iris patterns. Abiyev and Altunkaya [9] presented a novel iris recognition system based on NNs for identifying individuals biometrically and also proposed a fast algorithm to localise iris boundaries. Cho and Kim [10] used 2D wavelet transform based on Haar wavelets for feature extraction to recognise the human iris and applied Learning Vector Quantization (LVQ) NN for classification purposes.…”

Section: Related and Background Workmentioning

confidence: 99%

Hybrid robust iris recognition approach using iris image pre‐processing, two‐dimensional gabor features and multi‐layer perceptron neural network/PSO

Ahmadi

Akbarizadeh

2017

IET biom.

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Computational intelligence is employed to solve factual and complicated global problems, though neural networks (NNs) and evolutionary computing have also affected these issues. Biometric traits are applicable for detecting crime in security systems because they offer attractive features such as stability and uniqueness. Although various methods have been proposed for this objective, feature shortcomings such as computational complexity, long run times, and high memory consumption remain. The current study proposes a novel human iris recognition approach based on a multi-layer perceptron NN and particle swarm optimisation (PSO) algorithms to train the network in order to increase generalisation performance. A combination of these algorithms was used as a classifier. A pre-processing step was performed on the iris images to improve the results and two-dimensional gabor kernel feature extraction was applied. The data was normalised, trained, and tested using the proposed method. A PSO algorithm was applied to train the NN for data classification. The experimental results show that the proposed method performs better than many other well-known techniques. The benchmark Chinese Academy of Science and Institute of Automation (CASIA)-iris V3 and Center for Machine Learning and Intelligent Systems at the University of California, Irvine (UCI) machine learning repository datasets were used for testing and comparison.

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“…Due to the ability of back propagation networks to learn complicated multi‐dimensional mapping, it is widely applied NN architecture. The back propagation NN architecture is consisting of fully interconnected layers or rows of processing units [36]. The features are the input signal for NN.…”

Section: Proposed Systemmentioning

confidence: 99%

Super resolution and recognition of long range captured multi‐frame iris images

Deshpande¹,

Patavardhan²

2017

IET biom.

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In this study, a framework to super resolve and recognise the long range captured iris polar images is proposed. The proposed framework consists of best frame selection algorithm, modified diamond search algorithm, Gaussian process regression (GPR) based and enhanced iterated back projection (EIBP)-based super-resolution approach, fuzzy entropy-based feature selector and neural network (NN) classifier. The framework uses linear kernel co-variance function in local patch-based GPR and EIBP algorithms and it super resolves the iris images depending on the contents of the patches, without an external dataset. NN classifier classifies the iris images by using features extracted using discrete cosine transform domain based noreference image quality assessment model, Gray level co-occurrence matrix, Hu seven moments and statistical features. The framework is tested using CASIA long range iris database by comparing and analysing the peak signal-to-noise ratio, structural similarity index matrix and visual information fidelity in pixel domain of proposed algorithms with Yang and Nguyen framework. The results demonstrate that the proposed framework is well suited for recognition of iris images captured at a long distance.

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Neural network based biometric personal identification with fast iris segmentation

Cited by 24 publications

References 23 publications

Deep Convolutional Neural Networks for Chest Diseases Detection

Deep Convolutional Neural Networks for Chest Diseases Detection

Hybrid robust iris recognition approach using iris image pre‐processing, two‐dimensional gabor features and multi‐layer perceptron neural network/PSO

Super resolution and recognition of long range captured multi‐frame iris images

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