Abstract
Palmprint-based biometric recognition has received tremendous attention due to its several advantages such as high security, non-invasive and good hygiene propensities. Recent deep convolutional neural network (CNN) has been successfully applied for palmprint recognition and achieved promising performance due to its breakthroughs in image classification, which however usually requires a massive amount of labeled samples to finetune the network. In this paper, we propose a compact CNN with limited layers for palmprint recognition by embedding double attention mechanisms into the convolutional layers. Specifically, we first design a channel attention module to learn and select the discriminative channel maps by adaptively optimizing the attention weights among all channels. Then, we engineer a location attention module to learn the position-specific features of the palmprints. Both the channel and location attention modules are subsequently embedded into each convolutional layer, such that more discriminative features can be efficiently exploited during feature learning. Lastly, we train a fully convolutional network as the classifier for feature identification. Extensive experimental results on three widely used databases demonstrate the effectiveness of the proposed method in comparison with the state-of-the-arts.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Fei, L., Zhang, B., Tian, C., Teng, S., Wen, J.: Jointly learning multi-instance hand-based biometric descriptor. Inf. Sci. 562, 1–12 (2021)
Hu, G., Yang, Y., Dong, Y., Kittler, J., Hospedales, T.: When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In: IEEE International Conference on Computer Vision Workshop, pp. 384–392 (2015)
Wang, R., Han, C., Wu, Y., Guo, T.: Fingerprint classification based on depth neural network. Comput. Sci. 137(25), 640–641 (2014)
Xu, Y., Fei, L., Wen, J., Zhang, D.: Discriminative and robust competitive code for palmprint recognition. IEEE Trans. Syst. Man Cybern. Syst. 48(2), 232–241 (2018)
Qiu, Z., Fei, L., Teng, S., Zhang, W., Jia, W.: Local discriminative direction extraction for palmprint recognition. In: Chinese Conference on Biometrics Recognition, pp. 3–11 (2019)
Zhao, S., Zhang, B.: Learning salient and discriminative descriptor for palmprint feature extraction and identification. IEEE Trans. Neural Netw. Learn. Syst. 31(12), 5219–5230 (2020)
Zhao, S., Zhang, B.: Learning complete and discriminative direction pattern for robust palmprint recognition. IEEE Trans. Image Process. 30, 1001–1014 (2021)
Fei, L., Lu, G., Jia, W., Teng, S., Zhang, D.: Feature extraction methods for palmprint recognition: a survey and evaluation. IEEE Trans. Syst. Man Cybern. Syst. 49(2), 346–363 (2019)
Rida, I., Al-Maadeed, S., Mahmood, A., Bouridane, A., Bakshi, S.: Palmprint identification using an ensemble of sparse representations. IEEE Access 6, 3241–3248 (2018)
Liang, L., Chen, T., Fei, L.: Orientation space code and multi-feature two-phase sparse representation for palmprint recognition. Int. J. Mach. Learn. Cybern. 11(7), 1453–1461 (2019). https://doi.org/10.1007/s13042-019-01049-7
Li, S., Zhang, B.: Joint discriminative sparse coding for robust hand-based multimodal recognition. IEEE Trans. Inf. Forensics Secur. 16, 3186–3198 (2021)
Minaee, S., Wang, Y.: Palmprint recognition using deep scattering network. In: IEEE International Symposium on Circuits and Systems, pp. 1–4 (2017)
Shao, H., Zhong, D., Du, X.: Deep distillation hashing for unconstrained palmprint recognition. IEEE Trans. Instrum. Meas. 70, 1–13 (2021)
Jia, W., Gao, J., Xia, W., Zhao, Y., Min, H., Lu, J.T.: A performance evaluation of classic convolutional neural networks for 2d and 3d palmprint and palm vein recognition. Int. J. Autom. Comput. 18(1), 18–44 (2021)
Dian, L., Dongmei, S.: Contactless palmprint recognition based on convolutional neural network. In: International Conference on Signal Processing, pp. 1363–1367 (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. In: Annual Conference on Neural Information Processing Systems, pp. 1106–1114 (2012)
Tarawneh, A.S., Chetverikov, D., Hassanat, A.B.: Pilot comparative study of different deep features for palmprint identification in low-quality images. In: Hungarian Conference on Computer Graphics and Geometry (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)
Genovese, A., Piuri, V., Plataniotis, K.N., Scotti, F.: Palmnet: Gabor-PCA convolutional networks for touchless palmprint recognition. IEEE Trans. Inf. Forensics Secur. 14(12), 3160–3174 (2019)
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011–2023 (2020)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: European Conference on Computer Vision (2018)
Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 510–519 (2019)
Sun, Z., Tan, T., Wang, Y., Li, S.: Ordinal palmprint represention for personal identification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 279–284 (2005)
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 Recogn. 69(1), 199–212 (2017)
Jia, W., et al.: Palmprint recognition based on complete direction representation. IEEE Trans. Image Process. 26(9), 4483–4498 (2017)
Zheng, Q., Kumar, A., Pan, G.: A 3d feature descriptor recovered from a single 2d palmprint image. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1272–1279 (2016)
Fei, L., Zhang, B., Xu, Y., Yan, L.: Palmprint recognition using neighboring direction indicator. IEEE Trans. Hum. Mach. Syst. 46(6), 298–787 (2016)
Acknowledgments
This work was supported in part by the Guangzhou Science and technology plan project under Grant 202002030110, in part by the Natural Science Foundation of Guangdong Province under Grant 2019A1515011811, and in part by the National Natural Science Foundation of China under Grants 62076086 and 61972102.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zheng, Y., Fei, L., Jia, W., Wen, J., Teng, S., Rida, I. (2021). Compact Double Attention Module Embedded CNN for Palmprint Recognition. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-89029-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89028-5
Online ISBN: 978-3-030-89029-2
eBook Packages: Computer ScienceComputer Science (R0)