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

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Abstract

Palm print scanning is a widespread method for biometric identity detection which has some advantages over other methods including its simplicity and relatively lower cost. In this study, a novel methods for biometric verification and identification by contactless palm scanning technique is proposed. In the study, Ripplet-I Transform (R-IT) which is a generalized form of Curvelet Transform (CuT), have been used in addition to multi-resolution transforms which were previously used in the literature as palm print verification and identification methods such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Contourlet Transform (CoT). In addition, Principal Component Analysis (PCA) and Local Binary Pattern (LBP) have been utilized to increase the algorithm diversity. In order to investigate the effect of classification methods on the study results and the processing times, Artificial Neural Network (ANN), Euclidean Distance (ED) and Support Vector Machine (SVM) have been used separately for matching in the verification part of study. The performance of Convolutional Neural Network (CNN) as a classifier has also been examined. Verification and identification algorithms proposed in the study have been tested using palm print images of Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database (Version 1.0). The studies, that were carried out under two main sections yielded interesting results. At the end of the study, AUC (Area Under the ROC Curve) values ranging from 0.550 (Equal Error Rate (EER)= 0.4594) to 0.9875 (EER= 0.0336) were obtained for palm print verification. The highest AUC value without using LBP was obtained as 0.9563 (EER= 0.1096) using R-IT/CuT+DCT+CNN. Study results were showed that CNN is more successful than other classifiers without using LBP. It also has pointed out that the R-IT/CuT provides better results than the DWT and CoT. Using LBP in algorithms has increased success for ED, SVM and ANN. However, it has reduced overall for CNN. The highest AUC value (0.9875 and EER= 0.0336) was provided by the LBP+DWT+ED algorithm for palm print verification. The highest Identification Rate (IR) was achieved by using the LBP+CoT+ED algorithm with 84.444% for for palm print identification.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Gazi University, Ankara, Turkey

    Firat Hardalac, Anil Akyel & Ugurhan Kutbay

  2. Ministry of Health of Republic of Turkey, Ankara, Turkey

    Hüseyin Yaşar

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  1. Firat Hardalac
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  2. Hüseyin Yaşar
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  4. Ugurhan Kutbay
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Correspondence to Hüseyin Yaşar.

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Dr. Hardalac declares that he has no conflict of interest. Mr. Yasar declares that he has no conflict of interest. Mr. Akyel declares that he has no conflict of interest. Dr. Kutbay declares that he has no conflict of interest.

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Hardalac, F., Yaşar, H., Akyel, A. et al. A novel comparative study using multi-resolution transforms and convolutional neural network (CNN) for contactless palm print verification and identification. Multimed Tools Appl 79, 22929–22963 (2020). https://doi.org/10.1007/s11042-020-09005-2

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