A novel comparative study using multi-resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification

Hardalaç, Fırat; Yaşar, Hüseyin; Akyel, Anıl; Kutbay, Uğurhan

doi:10.1007/s11042-020-09005-2

Cited by 17 publications

(17 citation statements)

References 47 publications

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“…In this context, a significant part of the best results obtained in the study was provided using the pipeline classification algorithm. In this sense, it can be seen that the results of the study support some other literature studies [ 61 – 66 ] where the CNN and LBP methods are used together and use of the LBP was shown to increase the success of the relevant study.…”

Section: Discussionsupporting

confidence: 85%

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar

Ceylan

2020

Appl Intell

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In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

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Section: Discussionsupporting

confidence: 85%

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Yaşar

Ceylan

2020

Appl Intell

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“…The results support different studies [ 44 – 49 ] where the use of CNN and LBP methods applied together and the use of LBP increased the success of the relevant studies. However, in those studies, the use of LBP appears to be a factor that directly improves the results.…”

Section: Discussionsupporting

confidence: 88%

“…There are some previous studies in which LBP and DT-CWT methods were combined with CNN. LBP and DT-CWT were used in the recognition of palmprint images in a study carried out by Hardalac et al [ 44 ]. In that study, it was shown that using LBP as a pre-treatment improves its results.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Yaşar

Ceylan

2021

Cogn Comput

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Patients infected with the COVID-19 virus develop severe pneumonia, which generally leads to death. Radiological evidence has demonstrated that the disease causes interstitial involvement in the lungs and lung opacities, as well as bilateral groundglass opacities and patchy opacities. In this study, new pipeline suggestions are presented, and their performance is tested to decrease the number of false-negative (FN), false-positive (FP), and total misclassified images (FN + FP) in the diagnosis of COVID-19 (COVID-19/non-COVID-19 and COVID-19 pneumonia/other pneumonia) from CT lung images. A total of 4320 CT lung images, of which 2554 were related to COVID-19 and 1766 to non-COVID-19, were used for the test procedures in COVID-19 and non-COVID-19 classifications. Similarly, a total of 3801 CT lung images, of which 2554 were related to COVID-19 pneumonia and 1247 to other pneumonia, were used for the test procedures in COVID-19 pneumonia and other pneumonia classifications. A 24-layer convolutional neural network (CNN) architecture was used for the classification processes. Within the scope of this study, the results of two experiments were obtained by using CT lung images with and without local binary pattern (LBP) application, and sub-band images were obtained by applying dual-tree complex wavelet transform (DT-CWT) to these images. Next, new classification results were calculated from these two results by using the five pipeline approaches presented in this study. For COVID-19 and non-COVID-19 classification, the highest sensitivity, specificity, accuracy, F-1, and AUC values obtained without using pipeline approaches were 0.

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“…Holistic Feature Extraction A. Subspace Method [50], [64], [66], [77][27], [95] -Unsupervised linear method Application of PCA and other unsupervised subspace methods [70] -Supervised linear method PCA+LDA on raw data [24], [93] -Kernel method Applications of kernel PCA and kernel fisher discriminant [96], [54] Transform domain subspace method Subspace methods in the transform domains [6], [8] B. Invariant Moment Zernike moments And Hu Invariant moment [82] C. Spectral Representation Wavelet Signature Global statistical signatures in the wavelet domain [25], [87] Correlation filter Advanced correlation filter Classifier Design [16] The subspace method's performance can be enhanced further by using the image transform. After this, the transform coefficients may be effectively used to recognize palmprint and robust variability within the class.…”

Section: Reference Approach Descriptionmentioning

confidence: 99%

Contactless Palmprint Recognition System: A Survey

et al. 2022

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Information systems in organizations traditionally require users to remember their secret pins (password), token, card number, or both to confirm their identities. However, the technological trend has been moving towards personal identification based on individual behavioural attributes (such as gaits, signature, and voice) or physiological (such as palmprint, fingerprint, face, iris, or ear). These attributes (biometrics) offer many advantages over knowledge and possession-based approaches. For example, palmprint images have rich, unique features for reliable human identification, and it has received significant attention due to their stability, reliability, uniqueness, and non-intrusiveness. This paper provides an overview and evaluation of contactless palmprint recognition system, the state-of-the-art performance of existing works, different types of "Region of Interest" (ROI) extraction algorithms, feature extraction, and matching algorithms. Finally, the findings obtained are presented and discussed.INDEX TERMS biometrics, information system, palmprint images, region of interest

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A novel comparative study using multi-resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification

Cited by 17 publications

References 47 publications

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

A new deep learning pipeline to detect Covid-19 on chest X-ray images using local binary pattern, dual tree complex wavelet transform and convolutional neural networks

Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Contactless Palmprint Recognition System: A Survey

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