Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Contactless Palmprint Identification Using the Pretrained VGGNet Model

사전 학습된 VGGNet 모델을 이용한 비접촉 장문 인식

  • Kim, Min-Ki (Dept. of Computer Science, Gyeongsang National University Engineering Research Institute)
  • Received : 2018.10.31
  • Accepted : 2018.11.28
  • Published : 2018.12.31
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Abstract

Palm image acquisition without contact has advantages in user convenience and hygienic issues, but such images generally display more image variations than those acquired employing a contact plate or pegs. Therefore, it is necessary to develop a palmprint identification method which is robust to affine variations. This study proposes a deep learning approach which can effectively identify contactless palmprints. In general, it is very difficult to collect enough volume of palmprint images for training a deep convolutional neural network(DCNN). So we adopted an approach to use a pretrained DCNN. We designed two new DCNNs based on the VGGNet. One combines the VGGNet with SVM. The other add a shallow network on the middle-level of the VGGNet. The experimental results with two public palmprint databases show that the proposed method performs well not only contact-based palmprints but also contactless palmprints.

Keywords

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Fig. 1. Architecture of VGGNet [21].

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Fig. 2. ROI extraction: (a) input image, (b) binarized image, (c) feature points to extract ROI, (d) normalized ROI image.

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Fig. 3. Image enhancement: (a) ROI image, (b) Gabor filtered image, (b) equalized image, (c) smoothed image.

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Fig. 4. Overview of the proposed VGGmp4Svm.

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Fig. 5. Palmprint sample images selected at PolyU and Tongji dataset.

Table 1. Architecture of the newly added back-end of the VGGPalmNet

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Table 2. Palmprint identification results with the data of the first session

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Table 3. Palmprint identification results with the data of the second session

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Table 4. Performance comparison among the proposed methods

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Table 5. Performance comparison with different researches

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