Abstract
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.
Highlights
Biometric-based authentication has been discussed in a wide range of state-of-the-art research in the context of security applications
Thereafter, since the output of the ROI extraction module (REM) is a region in the input image, it is evaluated via computing the intersection over union (IoU) metric
The final output is evaluated via accuracy, receiver operating characteristic (ROC) curves, and equal error rate (EER)
Summary
Biometric-based authentication has been discussed in a wide range of state-of-the-art research in the context of security applications. There are several biometric characteristics (e.g., DNA, face, and palmprint) that can be exploited in authentication systems [1]. Chatfield et al [58] described a fast CNN architecture inspired by AlexNet [7] model They have shown that reducing the number of kernels in convolutional layers in their architecture does not impact the output performance in comparison to AlexNet. Figure 1 illustrates the aforementioned fast. This architecture is comprised of eight layers, including five convolutional layers The input image of this architecture is 224 × 224 × 3 and the structure of the five convolutional layers are as follows: (i) 64 kernels of size 11 × 11 × 3 in the first layer The input image of this architecture is 224 × 224 × 3 and the structure of the five convolutional layers are as follows: (i) 64 kernels of size 11 × 11 × 3 in the first layer (Conv. 1 in Figure 1), (ii) 256 kernels of size 5 × 5 × 64 in the second layer (Conv. 2), and (iii) 256 kernels of size 3 × 3 × 256 in the three layers (Conv. 3, Conv. 4, and Conv. 5)
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