Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning
Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural netw...
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MDPI AG
2018-07-01
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| Series: | Applied Sciences |
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| Online Access: | http://www.mdpi.com/2076-3417/8/7/1210 |
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doaj-2e6340e11354461180d4dd05826bf4a52020-11-24T21:49:05ZengMDPI AGApplied Sciences2076-34172018-07-0187121010.3390/app8071210app8071210Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer LearningMahdieh Izadpanahkakhk0Seyyed Mohammad Razavi1Mehran Taghipour-Gorjikolaie2Seyyed Hamid Zahiri3Aurelio Uncini4Department of Electrical and Computer Engineering, University of Birjand, Birjand 971481151, IranDepartment of Electrical and Computer Engineering, University of Birjand, Birjand 971481151, IranDepartment of Electrical and Computer Engineering, University of Birjand, Birjand 971481151, IranDepartment of Electrical and Computer Engineering, University of Birjand, Birjand 971481151, IranDepartment of Information Engineering, Electronics and Telecommunications of the University of Rome “La Sapienza”, 00185 Rome, ItalyPalmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online.http://www.mdpi.com/2076-3417/8/7/1210region of interest extractionpalm print verificationdeep learningconvolutional neural networktransfer learningfeature extraction |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Mahdieh Izadpanahkakhk Seyyed Mohammad Razavi Mehran Taghipour-Gorjikolaie Seyyed Hamid Zahiri Aurelio Uncini |
| spellingShingle |
Mahdieh Izadpanahkakhk Seyyed Mohammad Razavi Mehran Taghipour-Gorjikolaie Seyyed Hamid Zahiri Aurelio Uncini Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning Applied Sciences region of interest extraction palm print verification deep learning convolutional neural network transfer learning feature extraction |
| author_facet |
Mahdieh Izadpanahkakhk Seyyed Mohammad Razavi Mehran Taghipour-Gorjikolaie Seyyed Hamid Zahiri Aurelio Uncini |
| author_sort |
Mahdieh Izadpanahkakhk |
| title |
Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning |
| title_short |
Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning |
| title_full |
Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning |
| title_fullStr |
Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning |
| title_full_unstemmed |
Deep Region of Interest and Feature Extraction Models for Palmprint Verification Using Convolutional Neural Networks Transfer Learning |
| title_sort |
deep region of interest and feature extraction models for palmprint verification using convolutional neural networks transfer learning |
| publisher |
MDPI AG |
| series |
Applied Sciences |
| issn |
2076-3417 |
| publishDate |
2018-07-01 |
| description |
Palmprint verification is one of the most significant and popular approaches for personal authentication due to its high accuracy and efficiency. Using deep region of interest (ROI) and feature extraction models for palmprint verification, a novel approach is proposed where convolutional neural networks (CNNs) along with transfer learning are exploited. The extracted palmprint ROIs are fed to the final verification system, which is composed of two modules. These modules are (i) a pre-trained CNN architecture as a feature extractor and (ii) a machine learning classifier. In order to evaluate our proposed model, we computed the intersection over union (IoU) metric for ROI extraction along with accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER) for the verification task.The experiments demonstrated that the ROI extraction module could significantly find the appropriate palmprint ROIs, and the verification results were crucially precise. This was verified by different databases and classification methods employed in our proposed model. In comparison with other existing approaches, our model was competitive with the state-of-the-art approaches that rely on the representation of hand-crafted descriptors. We achieved a IoU score of 93% and EER of 0.0125 using a support vector machine (SVM) classifier for the contact-based Hong Kong Polytechnic University Palmprint (HKPU) database. It is notable that all codes are open-source and can be accessed online. |
| topic |
region of interest extraction palm print verification deep learning convolutional neural network transfer learning feature extraction |
| url |
http://www.mdpi.com/2076-3417/8/7/1210 |
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