Abstract
Palmprint recognition has attracted widespread attention because of its advantages such as easy acquisition, rich texture, and security. However, most existing palmprint recognition methods focus most on feature extraction and matching without evaluating the quality of palmprint images, possibly leading to low recognition efficiency. In this paper, we propose a texture-guided multiscale feature learning network for palmprint image quality assessment. Specifically, we first employ a multiscale feature learning network to learn multiscale features. Then, we simultaneously use the multiscale features to learn image quality features by a QualityNet and texture features by a texture guided network. Texture features are then further used to learn texture quality features via TextureNet. Finally, we fuse the image quality features and texture quality features as palmprint quality features to predict the quality score via a regressor. Experimental results on the widely used palmprint database demonstrate that the proposed method consistently outperforms the state-of-the-art methods on palmprint image quality assessment.
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References
Jain, A.K., Ross, A., Pankanti, S.: Biometrics: a tool for information security. IEEE Trans. Inf. Forensics Secur. 1(2), 125–143 (2006)
Kong, W.K., Zhang, D., Li, W.: Palmprint feature extraction using 2-D Gabor filters. Pattern Recogn. 36(10), 2339–2347 (2003)
Fei, L., Zhang, B., Xu, Y., Tian, C., Imad, R., Zhang, D.: Jointly heterogeneous palmprint discriminant feature learning. IEEE Trans. Neural Netw. Learn. Syst. (2021). https://doi.org/10.1109/TNNLS.2021.3066381:1-12
Fei, L., Zhang, B., Jia, W., Wen, J., Zhang, D.: Feature extraction for 3-D palmprint recognition: a survey. IEEE Trans. Instrum. Meas. 69(3), 645–656 (2020)
Fei, L., Zhang, B., Zhang, L., Jia, W., Wen, J., Jigang, W.: Learning compact multifeature codes for palmprint recognition from a single training image per palm. IEEE Trans. Multimedia 23, 2930–2942 (2020)
Jia, W., Rongxiang, H., Lei, Y., Zhao, Y., Gui, J.: Histogram of oriented lines for palmprint recognition. IEEE Trans. Syst. Man Cybernet. Syst. 44(3), 385–395 (2013)
Ou, F., et al.: SDD-FIQA: unsupervised face image quality assessment with similarity distribution distance. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7670–7679 (2021)
Zhang, L., Li, L., Yang, A., Shen, Y., Yang, M.: Towards contactless palmprint recognition: a novel device, a new benchmark, and a collaborative representation based identification approach. Pattern Recognition 69, 199–212 (2017)
Su, S., et al.: Blindly assess image quality in the wild guided by a self-adaptive hyper network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3667–3676 (2020)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Fei, L., et al.: Jointly learning multiple curvature descriptor for 3D palmprint recognition. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 302–308. IEEE (2021)
Acknowledgments
This work was supported in part by the Guangzhou Science and technology plan project under Grant 202002030110, and in part by the National Natural Science Foundation of China under Grant 62176066 and Grant 62106052.
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Sun, X., Fei, L., Zhao, S., Li, S., Wen, J., Jia, W. (2022). Texture-Guided Multiscale Feature Learning Network for Palmprint Image Quality Assessment. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_56
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DOI: https://doi.org/10.1007/978-3-031-20233-9_56
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