{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:17:55Z","timestamp":1760149075635,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science and Technology Council of Funder","award":["110-2221-E-008-057-MY3"],"award-info":[{"award-number":["110-2221-E-008-057-MY3"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Biometrics has become an important research issue in recent years, and the use of deep learning neural networks has made it possible to develop more reliable and efficient recognition systems. Palms have been identified as one of the most promising candidates among various biometrics due to their unique features and easy accessibility. However, traditional palm recognition methods involve 3D point clouds, which can be complex and difficult to work with. To mitigate this challenge, this paper proposes two methods which are Multi-View Projection (MVP) and Light Inverted Residual Block (LIRB).The MVP simulates different angles that observers use to observe palms in reality. It transforms 3D point clouds into multiple 2D images and effectively reduces the loss of mapping 3D data to 2D data. Therefore, the MVP can greatly reduce the complexity of the system. In experiments, MVP demonstrated remarkable performance on various famous models, such as VGG or MobileNetv2, with a particular improvement in the performance of smaller models. To further improve the performance of small models, this paper applies LIRB to build a lightweight 2D CNN called Tiny-MobileNet (TMBNet).The TMBNet has only a few convolutional layers but outperforms the 3D baselines PointNet and PointNet++ in FLOPs and accuracy. The experimental results show that the proposed method can effectively mitigate the challenges of recognizing palms through 3D point clouds of palms. The proposed method not only reduces the complexity of the system but also extends the use of lightweight CNN. These findings have significant implications for developing biometrics and could lead to improvements in various fields, such as access control and security control.<\/jats:p>","DOI":"10.3390\/info14070381","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T01:38:32Z","timestamp":1688434712000},"page":"381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Develop a Lightweight Convolutional Neural Network to Recognize Palms Using 3D Point Clouds"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6675-0436","authenticated-orcid":false,"given":"Yu-Ming","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chia-Yuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0831-8251","authenticated-orcid":false,"given":"Chih-Lung","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Hwa Hsia University of Technology, New Taipei 173, Taiwan"},{"name":"Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 100, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Chieh","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuo-Chin","family":"Fan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Central University, Taoyuan 320, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"ref_1","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_4","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_5","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_6","first-page":"5105","article-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"Qi","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1049\/el:20071688","article-title":"Gabor-based kernel PCA for palmprint recognition","volume":"43","author":"Ekinci","year":"2007","journal-title":"Electron. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xu, X., and Guo, Z. (2010, January 22). Multispectral palmprint recognition using quaternion principal component analysis. Proceedings of the 2010 International Workshop on Emerging Techniques and Challenges for Hand-Based Biometrics, Istanbul, Turkey.","DOI":"10.1109\/ETCHB.2010.5559287"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dian, L., and Dongmei, S. (2016, January 6\u201310). Contactless palmprint recognition based on convolutional neural network. Proceedings of the 2016 IEEE 13th International Conference on Signal Processing (ICSP), Chengdu, China.","DOI":"10.1109\/ICSP.2016.7878049"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Svoboda, J., Masci, J., and Bronstein, M.M. (2016, January 4\u20138). Palmprint recognition via discriminative index learning. Proceedings of the 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900298"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TCSVT.2019.2904283","article-title":"Centralized large margin cosine loss for open-set deep palmprint recognition","volume":"30","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_12","unstructured":"Chen, W., Yu, Z., Wang, Z., and Anandkumar, A. (2020, January 13\u201318). Automated synthetic-to-real generalization. Proceedings of the International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_13","unstructured":"Zhao, K., Shen, L., Zhang, Y., Zhou, C., Wang, T., Zhang, R., Ding, S., Jia, W., and Shen, W. (2022). Lecture Notes in Computer Science, Part XIII, Proceedings of the Computer Vision\u2013ECCV 2022: 17th European Conference, Tel Aviv, Israel, 23\u201327 October 2022, Springer."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.1109\/TIFS.2011.2121062","article-title":"A unified framework for contactless hand verification","volume":"6","author":"Kanhangad","year":"2011","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Su, H., Nie\u00dfner, M., Dai, A., Yan, M., and Guibas, L.J. (2016, January 27\u201330). Volumetric and multi-view cnns for object classification on 3d data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.609"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., and Li, H. (2019, January 15\u201320). Pointrcnn: 3D object proposal generation and detection from point cloud. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref_17","unstructured":"Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., and Tomizuka, M. (2020). Lecture Notes in Computer Science, Part XXVIII 16, Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer."},{"key":"ref_18","unstructured":"Chen, Y., Hu, V.T., Gavves, E., Mensink, T., Mettes, P., Yang, P., and Snoek, C.G. (2020). Lecture Notes in Computer Science, Part III 16, Proceedings of the Computer Vision\u2014ECCV 2020: 16th European Conference, Glasgow, UK, 23\u201328 August 2020, Springer."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kim, S., Lee, S., Hwang, D., Lee, J., Hwang, S.J., and Kim, H.J. (2021, January 11\u201317). Point cloud augmentation with weighted local transformations. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00059"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lee, D., Lee, J., Lee, J., Lee, H., Lee, M., Woo, S., and Lee, S. (2021, January 20\u201325). Regularization strategy for point cloud via rigidly mixed sample. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01564"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1109\/TSMC.2018.2795609","article-title":"Feature extraction methods for palmprint recognition: A survey and evaluation","volume":"49","author":"Fei","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_22","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_24","unstructured":"Agarap, A.F. (2018). Deep learning using rectified linear units (relu). arXiv."},{"key":"ref_25","unstructured":"Zoph, B., and Le, Q.V. (2016). Neural architecture search with reinforcement learning. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Su, H., Maji, S., Kalogerakis, E., and Learned-Miller, E. (2015, January 7\u201313). Multi-view convolutional neural networks for 3d shape recognition. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.114"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, L., Zhu, S., Fu, H., Tan, P., and Tai, C.L. (2020, January 14\u201319). End-to-end learning local multi-view descriptors for 3d point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00199"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., and Li, H. (2020, January 14\u201319). Pv-rcnn: Point-voxel feature set abstraction for 3D object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 14\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/7\/381\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:05:16Z","timestamp":1760126716000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/7\/381"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,3]]},"references-count":30,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["info14070381"],"URL":"https:\/\/doi.org\/10.3390\/info14070381","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2023,7,3]]}}}