Research on the BP-network-based Iris Recognition

Dai, Fengzhi; Li, Fan; Yu, Chunyu; Liu, Bo; Bai, Ruixiang

doi:10.2991/jrnal.2015.2.3.6

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…The optimal structure of the recognition algorithm or the feature extraction algorithm and the iris features of each category under the iris training set are summarized by statistical learning or cognitive learning. The iris recognition is performed in a coded or codeless form, such as Gabor filter [4], Hamming distance [5], wavelet transform [6], neural network [7], the convolutional neural network [8] in the codeless feature learning architecture, eye feature cognitive [9], the ideal iris evidence theory through clustering methods [10], feature extraction parameter optimization based on statistical optimization [11], BP neural network supervised learning [12], concept cognition based on deep learning neural network [13] and bionic humanoid algorithm learning [14]. Although these methods have achieved good results in various experiments, there are still some problems in practical application.…”

Section: Cognitive Learning [3]mentioning

confidence: 99%

Statistical Cognitive Learning and Security Output Protocol for Multi-State Iris Recognition

Liu

Zhu

et al. 2019

IEEE Access

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In this paper, the research focuses on the constrained iris under different acquisition states, and trains the mixed data without manual label division. A multi-state iris multi-classification recognition method based on convolutional neural network fusion statistical cognitive learning is proposed. The recognition process is divided into an image processing module, a classification module and a result output module. The image processing module converts iris images into recognition tags through convolutional neural network. By combining the characteristics of cognitive learning and statistical learning, the classification module converts iris features of the same category to the convolutional neural network label parameters by data statistical, and forms a single iris cognitive concept, thereby designing a single-category recognizer. Multiple single recognizers are combined in parallel to perform multi-classification recognition by the codeless fusion recognition mode, the results are further encrypted by external encryption. The final classification result is exported through the result output module, which can improve the security of data transmission. After confirming stealing attacks, the result output security protocol is activated, and legitimate users who pass the reliable third-party authentication can still get correct results after changing the decryption key and the result output mapping path. In the multi-state iris recognition, the experimental results of JLU iris library demonstrate that the proposed method can ensure the correct rate of the single classifier certification. In multi-classification recognition, it can effectively cluster iris of the same category and distinguish different categories of irises. In addition, the method can also effectively respond to the stealing attack behavior.

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Section: Cognitive Learning [3]mentioning

confidence: 99%

Statistical Cognitive Learning and Security Output Protocol for Multi-State Iris Recognition

Liu

Zhu

et al. 2019

IEEE Access

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“…There are superior classification methods for iris recognition when the self-organizing Feature Map (SOFM) and the back propagation neural network (BPNN) are used independently on different datasets (Fengzhi, Li, Chunyu, Bo and Ruixiang, 2015;Shivaniand Rajeev, 2012;Savita, et. al., 2012).…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of back propagation neural network and self organising feature map for iris-based access control system

Akanni Ayotunde Oluyemisi,

Omidiora Elijah Olusayo,

Makinde Bukola Oyeladun

et al. 2023

Global J. Eng. Technol. Adv.

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Iris recognition is considered as one of the best biometric methods used for human identification and verification because of its unique features that differ from one person to another. Self-Organizing Feature Map (SOFM) and Back Propagation Neural Network (BPNN) are two techniques that have been used previously for iris recognition but their performance comparison has not been adequately investigated. This research, therefore, carried out a comparative analysis between SOFM and BPNN in iris recognition. Three hundred (300) iris images from one hundred and fifty (150) subjects were acquired from Covenant University Iris dataset. The acquired iris images were pre-processed by segmenting the iris portion using Hough Transform and were normalized using Daugman’s Rubber Sheet Model. Local Binary Pattern was used for feature extraction and dimension reduction of the iris images. Each of the two algorithms, SOFM and BPNN was used individually as iris image classifier, this technique was implemented using MATLAB (R2016a). The performance of the two iris classifiers was evaluated individually and compared at 0.75 threshold value based on Recognition Accuracy (RA), False Acceptance Rate (FAR), False Rejection Rate (FRR), Equal Error Rate (EER), Training Time (TT) and Recognition Time (RT). The SOFM and BPNN techniques were validated by carrying out a t-test to compare the differences between the two techniques at 5% significance level. The SOFM technique gave RA, FAR, FRR, EER, TT and RT of 97.14%, 1.67%, 3.75%, 3.18%, 104.47s and 81.98s, respectively, while the corresponding values for BPNN were 94.29%, 5.00%, 6.25%, 5.91%, 116.23s and 101.77s respectively. The P value between the SOFM and BPNN techniques was 0.002. This research outcome revealed that SOFM outperformed BPNN in iris recognition system with respect to RA, FAR, FRR, EER, TT and RT. The SOFM technique could be used for more robust iris recognition system than BPNN in security surveillance systems or other related systems.

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Research on the BP-network-based Iris Recognition

Cited by 2 publications

References 9 publications

Statistical Cognitive Learning and Security Output Protocol for Multi-State Iris Recognition

Statistical Cognitive Learning and Security Output Protocol for Multi-State Iris Recognition

Comparative assessment of back propagation neural network and self organising feature map for iris-based access control system

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