Application of convolution neural network in Iris recognition technology

Zhang, Wenqiang; Wang, Changyuan; Xue, Pengxiang

doi:10.1109/icsai.2017.8248462

Cited by 9 publications

(9 citation statements)

References 3 publications

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“…In the fourth step, the iris image is exposed to a sequence of top hat and bottom hat filters, as suggested by Bai [94]. Zhang et al [95] in contrast applied the homomorphic filtering method as was suggested by Zhang and Shen [96] to make the iris image have a robust contrast and to overcome the occlusion impact [96]. However, if the iris images were generated by computer, the specular highlights from the eye images need to be eliminated [95].…”

Section: Histogram and Filtering Methodmentioning

confidence: 99%

“…Zhang et al [95] in contrast applied the homomorphic filtering method as was suggested by Zhang and Shen [96] to make the iris image have a robust contrast and to overcome the occlusion impact [96]. However, if the iris images were generated by computer, the specular highlights from the eye images need to be eliminated [95]. is can be done by a technique that explores discrepancies of the eye region as proposed by Carvalho et al [97].…”

Section: Histogram and Filtering Methodmentioning

confidence: 99%

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Iris Recognition Development Techniques: A Comprehensive Review

2021

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Recently, iris recognition techniques have achieved great performance in identification. Among authentication techniques, iris recognition systems have received attention very much due to their rich iris texture which gives robust standards for identifying individuals. Notwithstanding this, there are several challenges in unrestricted recognition environments. In this article, the researchers present the techniques used in different phases of the recognition system of the iris image. The researchers also reviewed the methods associated with each phase. The recognition system is divided into seven phases, namely, the acquisition phase in which the iris images are acquired, the preprocessing phase in which the quality of the iris image is improved, the segmentation phase in which the iris region is separated from the background of the image, the normalization phase in which the segmented iris region is shaped into a rectangle, the feature extraction phase in which the features of the iris region are extracted, the feature selection phase in which the unique features of the iris are selected using feature selection techniques, and finally the classification phase in which the iris images are classified. This article also explains the two approaches of iris recognition which are the traditional approach and the deep learning approach. In addition, the researchers discuss the advantages and disadvantages of previous techniques as well as the limitations and benefits of both the traditional and deep learning approaches of iris recognition. This study can be considered as an initial step towards a large-scale study about iris recognition.

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Section: Histogram and Filtering Methodmentioning

confidence: 99%

Section: Histogram and Filtering Methodmentioning

confidence: 99%

Iris Recognition Development Techniques: A Comprehensive Review

2021

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“…In the same year, Abhishek Gangwar and Akanksha Joshi et al proposed two CNN architectures, DeepIrisNet-A and DeepIrisNet-B for iris recognition. In 2017, Zhang et al 19 proposed an iris recognition method based on a convolutional neural network, which consists of three convolutional layers, three pooling layers, and two fully connected layers. The cross-entropy loss function is used to calculate the loss of the prediction result.…”

Section: Deep-learning Based Approachesmentioning

confidence: 99%

Iris recognition based on few‐shot learning

Lei

Xiao

et al. 2021

Computer Animation & Virtual

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Iris recognition is a popular research field in the biometrics, and it plays an important role in automatic recognition. Given sufficient training data, some deep learning-based approaches have achieved good performance on iris recognition. However, when the training data are limited, overfitting may occur. To address this issue, in this paper, we proposed a few-shot learning approach for iris recognition, based on model-agnostic meta-learning (MAML). To our best knowledge, we are the first to apply few-shot learning for iris recognition. Our experiments on the benchmark datasets have demonstrated that the proposed approach can achieve higher performance than the original MAML, and it is competitive to deep learning-based approaches.

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“…So in future, the approach can be adopted for visible spectrum images to recognize iris. Wenqiang et al, [17] employed CNN to train iris data. The NN uses only two connection layer, which decreases the number of parameters in the network and improves the training speed.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

Harakannanavar¹,

Prashanth²,

Raja³

2019

IJEAT

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Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

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Application of convolution neural network in Iris recognition technology

Cited by 9 publications

References 3 publications

Iris Recognition Development Techniques: A Comprehensive Review

Iris Recognition Development Techniques: A Comprehensive Review

Iris recognition based on few‐shot learning

Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

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