Iris-based Biometric Recognition using Modified Convolutional Neural Network

Le-Tien, Thuong; Phan-Xuan, Hanh; Nguyen-Duy, Phu; Le-Ba, Loc

doi:10.1109/atc.2018.8587560

Cited by 11 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Experimental results show that the proposed ISC-PCA method substantially outperforms single image PCA and the eigenfaces [12] techniques. These results also show that the proposed algorithm gives highly competitive performance to those of learning based techniques, including PCA with Neural Network [13] and Modified Convolutional Neural Network [14] at significantly lower computational cost. The rest of the paper is organized as follows: Section II introduces the proposed ISC-PCA algorithm, Section III discusses the benchmark CASIA-Iris-Interval eye image dataset and the experimental results, and Section IV concludes the paper.…”

Section: Introductionmentioning

confidence: 64%

“…From Table III, it can be seen that the proposed ISC-PCA technique significantly outperforms all statistical PCA based methods and the PCA and neural network based technique [13]. Furthermore, the ISC-PCA algorithm gives competitive performance to that of the Modified Convolutional Neural Network algorithm [14]. The proposed ISC-PCA method achieves this competitive result at significantly less computational cost than the learning based algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, it has significant potential to further improve the performance of learning-based iris recognition algorithms when used as their primary feature extractor. To enable the reader to contrast the performance of the proposed ISC-PCA method against both statistical and learning based techniques, results for ISC-PCA, single image PCA, eigenfaces, PCA with Neural Network [13], and Modified Convolutional Neural Network [14] have been tabulated in III. Results with the methods presented in [3] and [4] are not presented, as those technique use a number of datasets for evaluation.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Intensity Separation based Iris Recognition Method using Curvelets and PCA

Ghaffari

Zarachoff

Sheikh-Akbari

et al. 2019

2019 8th Mediterranean Conference on Embedded Computing (MECO)

View full text Add to dashboard Cite

This paper presents a PCA-based iris recognition method called Intensity Separation Curvelet based PCA (ISC-PCA). The proposed method uses Canny Edge detection and the Hough transform to extract and rectangularize the iris from the input eye image. The second generation Fast Digital Curvelet Transform (FDCT) is then applied to the resulting image, dividing it into its subbands. The resulting complex subbands coefficients within the same level are concatenated, generating two single frames. The coefficients in each resulting frame are then normalized and evenly divided into a preselected number of bands. The coefficient matrices within each frame are then vectorized and concatenated, generating a single 2D matrix. Conventional PCA is then performed on the resulting 2D matrix extracting its eigenvectors which are used for iris matching. The Euclidean distance is used as a measure to quantify the closeness of different iris images. Experimental results on images from the CASIA-Iris-Interval benchmark eye image dataset show that the proposed ISC-PCA technique significantly outperforms the state of the art PCA based methods, and achieves competitive results to those of the learning based techniques.

show abstract

Section: Introductionmentioning

confidence: 64%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Intensity Separation based Iris Recognition Method using Curvelets and PCA

Ghaffari

Zarachoff

Sheikh-Akbari

et al. 2019

2019 8th Mediterranean Conference on Embedded Computing (MECO)

View full text Add to dashboard Cite

show abstract

“…By comparisons, feature enhancement seems to be not required for recognition systems, either for safety or affordability. The differences among the proposed design and that of other experiments reported in [19,20] are described in Table V; the accuracy of the proposed design was higher. In addition, the iris ROI can be segmented more precisely because the proposed design was susceptible to interference from eyelashes and eyelids during segmentation.…”

Section: System Affordability Analysismentioning

confidence: 75%

“…In Ref. [18], the feature vector of the iris was identified using a shallow neural network, and Convolutional Neural Network (CNN)-based classifiers were also adopted for iris identification by Thuong et al [19]. According to Ref.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Hsiao,

Chang,

Fan

2024

JAIT

View full text Add to dashboard Cite

In this study, the proposed iris recognition method uses the You Only Look Once (YOLO)-based deep learning algorithm with the procedure divided into two stages. After extraction of the iris and pupil from the images, the iris Region of Interest (ROI) is identified by the classifier. Iris localization, iris segmentation, and feature enhancement are three crucial processes when extracting the iris ROI, and they constitute the first stage. Iris localization is firstly discussed, and the three methods are proposed with the system performance analyzed from the perspective of both system safety and affordability. The main difference among these methods is their complexity. Iris segmentation is then introduced, and an experiment is conducted to evaluate system performance when images are preprocessed for inputs by different segmentation methods, including images with and without normalization. Normalization and its necessary or unnecessary role in identifying images with deep learning are then analyzed. Finally, an examination of how feature enhancement influences the results of the proposed method is outlined. For system safety analysis, the Equal Error Rate (EER) of the proposed design approaches near zero; for system affordability analysis, the accuracy of the proposed design can be up to 98%.

show abstract

Presentation Attack Detection Framework

Shahriar

Etienne

2020

Studies in Computational Intelligence

View full text Add to dashboard Cite

Iris-based Biometric Recognition using Modified Convolutional Neural Network

Cited by 11 publications

References 12 publications

Intensity Separation based Iris Recognition Method using Curvelets and PCA

Intensity Separation based Iris Recognition Method using Curvelets and PCA

Two-Stage Deep Learning Technology Based Iris Recognition Methodology for Biometric Authorization

Presentation Attack Detection Framework

Contact Info

Product

Resources

About