{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:42:17Z","timestamp":1764175337619,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61673316","62177042","61901530"],"award-info":[{"award-number":["61673316","62177042","61901530"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2021JJ50137","2020JJ5767"],"award-info":[{"award-number":["2021JJ50137","2020JJ5767"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Training convolutional neural networks (CNN) often require a large amount of data. However, for some biometric data, such as fingerprints and iris, it is often difficult to obtain a large amount of data due to privacy issues. Therefore, training the CNN model often suffers from specific problems, such as overfitting, low accuracy, poor generalization ability, etc. To solve them, we propose a novel image augmentation algorithm for small sample iris image in this article. It is based on a modified sparrow search algorithm (SSA) called chaotic Pareto sparrow search algorithm (CPSSA), combined with contrast limited adaptive histogram equalization (CLAHE). The CPSSA is used to search for a group of clipping limit values. Then a set of iris images that satisfies the constraint condition is produced by CLAHE. In the fitness function, cosine similarity is used to ensure that the generated images are in the same class as the original one. We select 200 categories of iris images from the CASIA-Iris-Thousand dataset and test the proposed augmentation method on four CNN models. The experimental results show that, compared with the some standard image augmentation methods such as flipping, mirroring and clipping, the accuracy and Equal Error Rate (EER)of the proposed method have been significantly improved. The accuracy and EER of the CNN models with the best recognition performance can reach 95.5 and 0.6809 respectively. This fully shows that the data augmentation method proposed in this paper is effective and quite simple to implement.<\/jats:p>","DOI":"10.1007\/s44196-022-00173-7","type":"journal-article","created":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T11:03:21Z","timestamp":1671188601000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Data Augmentation for Small Sample Iris Image Based on a Modified Sparrow Search Algorithm"],"prefix":"10.1007","volume":"15","author":[{"given":"Qi","family":"Xiong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinman","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5190-4841","authenticated-orcid":false,"given":"Shaobo","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"173_CR1","doi-asserted-by":"publisher","first-page":"122134","DOI":"10.1109\/ACCESS.2019.2937809","volume":"7","author":"MB Lee","year":"2019","unstructured":"Lee, M.B., Kim, Y.H., Park, K.R.: Conditional generative adversarial network-based data augmentation for enhancement of Iris recognition accuracy. IEEE Access 7, 122134\u2013122152 (2019)","journal-title":"IEEE Access"},{"key":"173_CR2","doi-asserted-by":"publisher","first-page":"2840289","DOI":"10.1155\/2021\/2840289","volume":"2021","author":"M Chen","year":"2021","unstructured":"Chen, M., Wang, Y., Qin, Z., Zhu, X.: Few-shot website fingerprinting attack with data augmentation. Secur. Commun. Netw. 2021, 2840289 (2021)","journal-title":"Secur. Commun. Netw."},{"key":"173_CR3","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.neunet.2019.07.020","volume":"121","author":"V Varkarakis","year":"2020","unstructured":"Varkarakis, V., Bazrafkan, S., Corcoran, P.: Deep neural network and data augmentation methodology for off-axis Iris segmentation in wearable headsets. Neural Netw. 121, 101\u2013121 (2020)","journal-title":"Neural Netw."},{"key":"173_CR4","doi-asserted-by":"publisher","first-page":"2850632","DOI":"10.1155\/2018\/2850632","volume":"2018","author":"M Sajid","year":"2018","unstructured":"Sajid, M., Ali, N., Dar, S.H., et al.: Data augmentation-assisted makeup-invariant face recognition. Math. Probl. Eng. 2018, 2850632 (2018)","journal-title":"Math. Probl. Eng."},{"key":"173_CR5","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., Jitpattanakul, A.: Convolutional neural network and data augmentation for behavioral-based biometric user identification. In: ICT Systems and Sustainability, pp. 753\u2013761. Springer (2021)","DOI":"10.1007\/978-981-15-8289-9_72"},{"issue":"1","key":"173_CR6","doi-asserted-by":"publisher","first-page":"1871","DOI":"10.2991\/ijcis.d.210622.003","volume":"14","author":"J Zhang","year":"2021","unstructured":"Zhang, J., Han, F., Chun, Y., Liu, K., Chen, W.: Detecting objects from no-object regions: a context-based data augmentation for object detection. Int. J. Comput. Intell. Syst. 14(1), 1871\u20131879 (2021)","journal-title":"Int. J. Comput. Intell. Syst."},{"issue":"1","key":"173_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1\u201348 (2019)","journal-title":"J. Big Data"},{"issue":"11","key":"173_CR8","doi-asserted-by":"publisher","first-page":"2897","DOI":"10.1109\/TIFS.2018.2833033","volume":"13","author":"Q Zhang","year":"2018","unstructured":"Zhang, Q., Li, H., Sun, Z., Tan, T.: Deep feature fusion for iris and periocular biometrics on mobile devices. IEEE Trans. Inf. Forens. Secur. 13(11), 2897\u20132912 (2018)","journal-title":"IEEE Trans. Inf. Forens. Security"},{"issue":"12","key":"173_CR9","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.3390\/s17122933","volume":"17","author":"MB Lee","year":"2017","unstructured":"Lee, M.B., Hong, H.G., Park, K.R.: Noisy ocular recognition based on three convolutional neural networks. Sensors 17(12), 2933 (2017)","journal-title":"Sensors"},{"issue":"1","key":"173_CR10","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1002\/ima.22214","volume":"27","author":"V Magudeeswaran","year":"2017","unstructured":"Magudeeswaran, V., Fenshia Singh, J.: Contrast limited fuzzy adaptive histogram equalization for enhancement of brain images. Int. J. Imaging Syst. Technol. 27(1), 98\u2013103 (2017)","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"173_CR11","doi-asserted-by":"crossref","unstructured":"Soni, B., Mathur, P.: An improved image dehazing technique using Clahe and guided filter. In: 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 902\u2013907. IEEE (2020)","DOI":"10.1109\/SPIN48934.2020.9071296"},{"key":"173_CR12","doi-asserted-by":"publisher","first-page":"163395","DOI":"10.1109\/ACCESS.2019.2952545","volume":"7","author":"C Li","year":"2019","unstructured":"Li, C., Liu, J., Liu, A., Wu, Q., Bi, L.: Global and adaptive contrast enhancement for low illumination gray images. IEEE Access 7, 163395\u2013163411 (2019)","journal-title":"IEEE Access"},{"issue":"2","key":"173_CR13","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1007\/s11042-020-09752-2","volume":"80","author":"N Hassan","year":"2021","unstructured":"Hassan, N., Ullah, S., Bhatti, N., Mahmood, H., Zia, M.: The Retinex based improved underwater image enhancement. Multimed. Tools Appl. 80(2), 1839\u20131857 (2021)","journal-title":"Multimed. Tools Appl."},{"key":"173_CR14","doi-asserted-by":"crossref","unstructured":"Sanagavarapu, S., Sridhar, S., Gopal, T.V: Covid-19 identification in Clahe enhanced CT scans with class imbalance using ensembled ResNets. In 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp. 1\u20137. IEEE (2021)","DOI":"10.1109\/IEMTRONICS52119.2021.9422556"},{"key":"173_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2020.105928","volume":"31","author":"M-L Huang","year":"2020","unstructured":"Huang, M.-L., Lin, T.-Yu.: Dataset of breast mammography images with masses. Data Brief 31, 105928 (2020)","journal-title":"Data Brief"},{"issue":"22","key":"173_CR16","doi-asserted-by":"publisher","first-page":"15541","DOI":"10.1007\/s00521-021-06177-2","volume":"33","author":"E Tasci","year":"2021","unstructured":"Tasci, E., Uluturk, C., Ugur, A.: A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection. Neural Comput. Appl. 33(22), 15541\u201315555 (2021)","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"173_CR17","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.icte.2021.05.002","volume":"8","author":"SSM Sheet","year":"2022","unstructured":"Sheet, S.S.M., Tan, T.-S., As\u2019ari, M.A., Hitam, W.H.W., Sia, J.S.Y.: Retinal disease identification using upgraded CLAHE filter and transfer convolution neural network. ICT Express 8(1), 142\u2013150 (2022)","journal-title":"ICT Express"},{"issue":"1","key":"173_CR18","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/TCBB.2020.2986544","volume":"18","author":"X Yu","year":"2020","unstructured":"Yu, X., Kang, C., Guttery, D.S., Kadry, S., Chen, Y., Zhang, Y.-D.: ResNet-SCDA-50 for breast abnormality classification. IEEE\/ACM Trans. Comput. Biol. Bioinf. 18(1), 94\u2013102 (2020)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinf."},{"key":"173_CR19","doi-asserted-by":"crossref","unstructured":"Agustin, T., Utami, E., Hanif, A.F.: Implementation of data augmentation to improve performance CNN method for detecting diabetic retinopathy. In: 2020 3rd International Conference on Information and Communications Technology (ICOIACT), pp. 83\u201388. IEEE (2020)","DOI":"10.1109\/ICOIACT50329.2020.9332019"},{"key":"173_CR20","doi-asserted-by":"publisher","first-page":"11782","DOI":"10.1109\/ACCESS.2018.2797872","volume":"6","author":"Y Chang","year":"2018","unstructured":"Chang, Y., Jung, C., Ke, P., Song, H., Hwang, J.: Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6, 11782\u201311792 (2018)","journal-title":"IEEE Access"},{"key":"173_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106960","volume":"100","author":"B Song","year":"2021","unstructured":"Song, B., Wang, Z., Zou, L.: An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve. Appl. Soft Comput. 100, 106960 (2021)","journal-title":"Appl. Soft Comput."},{"key":"173_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114122","volume":"167","author":"D Zhao","year":"2021","unstructured":"Zhao, D., Liu, L., Yu, F., Heidari, A.A., Wang, M., Oliva, D., Muhammad, K., Chen, H.: Ant colony optimization with horizontal and vertical crossover search: fundamental visions for multi-threshold image segmentation. Expert Syst. Appl. 167, 114122 (2021)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"173_CR23","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1080\/21642583.2019.1708830","volume":"8","author":"J Xue","year":"2020","unstructured":"Xue, J., Shen, B.: A novel swarm intelligence optimization approach: sparrow search algorithm. Syst. Sci. Control Eng. 8(1), 22\u201334 (2020)","journal-title":"Syst. Sci. Control Eng."},{"issue":"21","key":"173_CR24","doi-asserted-by":"publisher","first-page":"2790","DOI":"10.3390\/math9212790","volume":"9","author":"Q Xiong","year":"2021","unstructured":"Xiong, Q., Zhang, X., He, S., Shen, J.: A fractional-order chaotic sparrow search algorithm for enhancement of long distance iris image. Mathematics 9(21), 2790 (2021)","journal-title":"Mathematics"},{"issue":"2","key":"173_CR25","doi-asserted-by":"publisher","first-page":"217","DOI":"10.3390\/electronics10020217","volume":"10","author":"Q Xiong","year":"2021","unstructured":"Xiong, Q., Zhang, X., Xu, X., He, S.: A modified chaotic binary particle swarm optimization scheme and its application in face-iris multimodal biometric identification. Electronics 10(2), 217 (2021)","journal-title":"Electronics"},{"issue":"3","key":"173_CR26","doi-asserted-by":"publisher","DOI":"10.1088\/1402-4896\/ab46c9","volume":"95","author":"S He","year":"2020","unstructured":"He, S., Sun, K., Wu, X.: Fractional symbolic network entropy analysis for the fractional-order chaotic systems. Phys. Scr. 95(3), 035220 (2020)","journal-title":"Phys. Scr."},{"issue":"1","key":"173_CR27","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/electronics9010085","volume":"9","author":"B Ammour","year":"2020","unstructured":"Ammour, B., Boubchir, L., Bouden, T., Ramdani, M.: Face-Iris multimodal biometric identification system. Electronics 9(1), 85 (2020)","journal-title":"Electronics"},{"key":"173_CR28","doi-asserted-by":"crossref","unstructured":"Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120\u2013133 (2016)","DOI":"10.1016\/j.knosys.2015.12.022"},{"key":"173_CR29","doi-asserted-by":"crossref","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6):84\u201390 (2012)","DOI":"10.1145\/3065386"},{"key":"173_CR30","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"173_CR31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"173_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"173_CR33","unstructured":"Casia Iris Image Database. Available Online. Accessed on 19 March 2022. http:\/\/biometrics.idealtest.org\/findDownloadDbByMode.do?mode=Iris,2014"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00173-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-022-00173-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-022-00173-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T11:07:28Z","timestamp":1671188848000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-022-00173-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["173"],"URL":"https:\/\/doi.org\/10.1007\/s44196-022-00173-7","relation":{},"ISSN":["1875-6883"],"issn-type":[{"type":"electronic","value":"1875-6883"}],"subject":[],"published":{"date-parts":[[2022,12,16]]},"assertion":[{"value":"9 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This work does not have any conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"110"}}