Cascade attention feature residual fusion network for iris localization and segmentation in non-cooperative environments
•Proposed a cascade attention feature residual fusion network (CA-RFNet).•Deep convolutional residual blocks in the encoder to enhance feature representation capability.•Cascade attention fusion modules in skip connections for adaptive multiscale feature fusion and information complementarity.•Bound...
Gespeichert in:
| Veröffentlicht in: | Displays Jg. 91; S. 103192 |
|---|---|
| Hauptverfasser: | , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
01.01.2026
|
| Schlagworte: | |
| ISSN: | 0141-9382 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | •Proposed a cascade attention feature residual fusion network (CA-RFNet).•Deep convolutional residual blocks in the encoder to enhance feature representation capability.•Cascade attention fusion modules in skip connections for adaptive multiscale feature fusion and information complementarity.•Boundary perception modules in the decoder to reinforce edge information.•Verified the localization and segmentation performance of CA-RFNet on iris datasets in non-cooperative environments.
Iris localization and segmentation constitute mission-critical preprocessing stages in iris recognition systems, where their precision directly governs overall recognition accuracy. However, iris images captured under non-cooperative conditions are prone to boundary distortions caused by eyelash or eyelid occlusions and defocus blurring, while texture features suffer from weakened saliency due to uneven illumination or specular reflections, leading to reduced algorithm robustness. To address these challenges, this paper proposes a cascade attention feature residual fusion network (CA-RFNet) for multitask iris localization and segmentation in unconstrained scenarios. CA-RFNet adopts an encoder-decoder structure with skip connections. In the encoder stage, deep convolutional residual blocks hierarchically extract iris texture features. A cascade attention fusion module embedded in skip connections dynamically weights and adaptively integrates multi-receptive-field features while enabling cross-scale information complementarity. The decoder incorporates a boundary perception module with cross-layer feature interaction mechanisms to enhance fine-grained structural perception and cross-hierarchy semantic representation, thereby improving edge prediction accuracy. CA-RFNet modules work collaboratively to overcome adverse effects of unconstrained subject behaviors and complex environmental interference on algorithm robustness in non-cooperative scenarios. Extensive experiments on five non-cooperative iris datasets (CASIA-Iris-Distance, CASIA-Iris-Complex-Occlusion, CASIA-Iris-Complex-Off-angle, CASIA-Iris-M1, and CASIA-Iris-Africa) demonstrate that CA-RFNet achieves superior segmentation and localization performance on challenging samples with complex noise factors including occlusion, off-angle, illumination variation, specular reflection, dark iris, and dark skin. |
|---|---|
| AbstractList | •Proposed a cascade attention feature residual fusion network (CA-RFNet).•Deep convolutional residual blocks in the encoder to enhance feature representation capability.•Cascade attention fusion modules in skip connections for adaptive multiscale feature fusion and information complementarity.•Boundary perception modules in the decoder to reinforce edge information.•Verified the localization and segmentation performance of CA-RFNet on iris datasets in non-cooperative environments.
Iris localization and segmentation constitute mission-critical preprocessing stages in iris recognition systems, where their precision directly governs overall recognition accuracy. However, iris images captured under non-cooperative conditions are prone to boundary distortions caused by eyelash or eyelid occlusions and defocus blurring, while texture features suffer from weakened saliency due to uneven illumination or specular reflections, leading to reduced algorithm robustness. To address these challenges, this paper proposes a cascade attention feature residual fusion network (CA-RFNet) for multitask iris localization and segmentation in unconstrained scenarios. CA-RFNet adopts an encoder-decoder structure with skip connections. In the encoder stage, deep convolutional residual blocks hierarchically extract iris texture features. A cascade attention fusion module embedded in skip connections dynamically weights and adaptively integrates multi-receptive-field features while enabling cross-scale information complementarity. The decoder incorporates a boundary perception module with cross-layer feature interaction mechanisms to enhance fine-grained structural perception and cross-hierarchy semantic representation, thereby improving edge prediction accuracy. CA-RFNet modules work collaboratively to overcome adverse effects of unconstrained subject behaviors and complex environmental interference on algorithm robustness in non-cooperative scenarios. Extensive experiments on five non-cooperative iris datasets (CASIA-Iris-Distance, CASIA-Iris-Complex-Occlusion, CASIA-Iris-Complex-Off-angle, CASIA-Iris-M1, and CASIA-Iris-Africa) demonstrate that CA-RFNet achieves superior segmentation and localization performance on challenging samples with complex noise factors including occlusion, off-angle, illumination variation, specular reflection, dark iris, and dark skin. |
| ArticleNumber | 103192 |
| Author | He, Changle Deng, Junkang Chen, Ying Chen, Huiling Guo, Shubin Zhu, Xiaodong Chen, Zhijie |
| Author_xml | – sequence: 1 givenname: Shubin surname: Guo fullname: Guo, Shubin email: gsb_0810@163.com organization: School of Software, Nanchang Hangkong University, Nanchang 330063, China – sequence: 2 givenname: Ying surname: Chen fullname: Chen, Ying email: c_y2008@163.com organization: School of Software, Nanchang Hangkong University, Nanchang 330063, China – sequence: 3 givenname: Junkang surname: Deng fullname: Deng, Junkang email: 1440945010@qq.com organization: School of Software, Nanchang Hangkong University, Nanchang 330063, China – sequence: 4 givenname: Huiling surname: Chen fullname: Chen, Huiling email: chenhuiling.jlu@gmail.com organization: Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China – sequence: 5 givenname: Zhijie surname: Chen fullname: Chen, Zhijie email: 2822972120@qq.com organization: School of Software, Nanchang Hangkong University, Nanchang 330063, China – sequence: 6 givenname: Changle surname: He fullname: He, Changle email: 1163968399@qq.com organization: School of Software, Nanchang Hangkong University, Nanchang 330063, China – sequence: 7 givenname: Xiaodong surname: Zhu fullname: Zhu, Xiaodong email: wsteqdzh@163.com organization: School of Software, Nanchang Hangkong University, Nanchang 330063, China |
| BookMark | eNp9kMtOwzAQRb0oEm3hD1j4B1Js106cDRKqeEmV2MDamtpj5JLalZ2Wx9eTEtasRrqaczVzZmQSU0RCrjhbcMbr6-3ChbLvYCGYUEO05K2YkCnjklftUotzMitlyxgTshFT8rmCYsEhhb7H2IcUqUfoDxlpxhLcATrqD-WUR-w_Un6nPmUacii0Sxa68A2_FERHC77thpIxCAORYmVT2mMeoiNSjMeQUzztlAty5qErePk35-T1_u5l9Vitnx-eVrfrygrV9JWsawVYe-2criX3stG8BdFYhrX2G6uU01JbPbznASyKjWylUs2wDK3aqOWcyLHX5lRKRm_2OewgfxnOzMmY2ZrRmDkZM6OxAbsZMRxuOwbMptiA0aILGW1vXAr_F_wAcN590A |
| Cites_doi | 10.1007/s10489-022-03973-8 10.1109/ICCV48922.2021.00986 10.11834/jig.210078 10.3390/electronics14020246 10.1109/ICCVW60793.2023.00361 10.1007/s42044-023-00157-6 10.1007/s13042-024-02232-1 10.1016/j.neucom.2022.10.064 10.1002/int.22649 10.1007/s00530-024-01280-5 10.1109/CCDC62350.2024.10588166 10.1007/978-3-031-20233-9_41 10.1007/s11042-021-11075-9 10.1007/978-3-319-24574-4_28 10.1142/S0219467824500426 10.1016/j.bspc.2025.107595 10.1109/TIFS.2020.2980791 10.1016/j.dsp.2021.103244 10.1109/ICSCN.2017.8085713 10.1117/1.JEI.31.5.053035 10.1007/978-3-031-91989-3_12 10.1109/TIFS.2023.3268504 10.1109/TIFS.2024.3407508 10.1109/CVPR.2018.00474 10.1109/CVPR.2018.00745 10.1109/CVPR.2016.90 10.1145/3651306 10.1134/S1054661824700743 10.1016/j.asoc.2025.113009 10.1109/TCSVT.2003.818350 |
| ContentType | Journal Article |
| Copyright | 2025 Elsevier B.V. |
| Copyright_xml | – notice: 2025 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.displa.2025.103192 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| ExternalDocumentID | 10_1016_j_displa_2025_103192 S014193822500229X |
| GroupedDBID | --K --M .DC .~1 0R~ 1B1 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AABXZ AAEDT AAEDW AAEPC AAFJI AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AATTM AAXKI AAXUO AAYFN AAYWO ABBOA ABFNM ABIVO ABJNI ABMAC ABMMH ABWVN ABXDB ABXRA ACDAQ ACGFS ACLOT ACNNM ACRLP ACRPL ACVFH ACZNC ADBBV ADCNI ADEZE ADJOM ADMUD ADNMO ADTZH AEBSH AECPX AEIPS AEKER AENEX AEUPX AEZYN AFJKZ AFPUW AFRZQ AFTJW AGHFR AGQPQ AGUBO AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIGII AIIUN AIKHN AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU AOMHK AOUOD APXCP ASPBG AVARZ AVWKF AXJTR AZFZN BJAXD BKOJK BLXMC CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA GBOLZ HLZ HVGLF HZ~ IHE J1W JJJVA KOM LG9 LY7 M41 MAGPM MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PRBVW Q38 R2- ROL RPZ SBC SDF SDG SES SET SEW SPC SPCBC SSB SSM SSO SST SSV SSZ T5K TN5 WUQ XPP ZMT ~G- ~HD 9DU AAYXX CITATION |
| ID | FETCH-LOGICAL-c257t-4665ae6f8dd8641f47819a27c0e68fbc55d848c8141faace2b494557864a95b53 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001563420500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0141-9382 |
| IngestDate | Sat Nov 29 06:55:51 EST 2025 Sat Sep 27 17:12:46 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | CA-RFNet Boundary perception module Iris localization and segmentation Cascade attention fusion module Deep convolutional residual block |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c257t-4665ae6f8dd8641f47819a27c0e68fbc55d848c8141faace2b494557864a95b53 |
| ParticipantIDs | crossref_primary_10_1016_j_displa_2025_103192 elsevier_sciencedirect_doi_10_1016_j_displa_2025_103192 |
| PublicationCentury | 2000 |
| PublicationDate | January 2026 2026-01-00 |
| PublicationDateYYYYMMDD | 2026-01-01 |
| PublicationDate_xml | – month: 01 year: 2026 text: January 2026 |
| PublicationDecade | 2020 |
| PublicationTitle | Displays |
| PublicationYear | 2026 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Yin, He, Zhang (b0015) 2025 Liu, Shen, Wu (b0160) 2023; 53 Jan, Alrashed, Min-Allah (b0055) 2024; 83 J. Hu, L. Shen, G. Sun, Squeeze‐and‐excitation networks[C], in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141. Z. Jia, J. Deng, L. Chi, et al., CondSeg: Ellipse estimation of pupil and iris via conditioned segmentation[J]. arXiv preprint arXiv:2408.17231, 2024. Guo, Chen, Zeng (b0070) 2022 Sun, Lu, Liu (b0150) 2022; 31 L. Yang, R.Y. Zhang, L. Li, et al., SimAM: A simple, parameter-free attention module for convolutional neural networks[C], in: Proceedings of the 38th International Conference on Machine Learning (PMLR). 2021, 139: 11863-11874. Samarin, Savelev, Toropov (b0185) 2024; 34 Wang, Muhammad, Wang (b0145) 2020; 15 S. Zhang, S. Zhang, Improved three-dimensional gaze estimation with precise iris segmentation based on YOLOv8[C], in: Proceedings of the 2024 36th Chinese Control and Decision Conference (CCDC), 2024, pp. 1970–1974. Bonyani, Ghanbari, Rad (b0105) 2024; 15 Pan, Xiong (b0045) 2021; 33 Avhad, Bakal (b0125) 2025; 105 Yan, Wang, Zhu (b0170) 2024; 30 He, Yang, Zheng (b0190) 2025; 55 K. He, X. Zhang, S. Ren, et al., Deep residual learning for image recognition[C], in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. Sun, He, Wang (b0005) 2021; 26 Pourafkham, Khotanlou (b0100) 2024 Daugman (b0040) 2004; 14 Jiang, Zhang, Wang (b0130) 2025; 14 Khaki (b0080) 2024; 24 A. Hossain, T. Sultan, S. Schuckers, Post-mortem human iris segmentation analysis with deep learning[J], 2024. arXiv preprint arXiv:2408.03448. M. Sandler, A. Howard, M. Zhu, et al., MobileNetV2: Inverted residuals and linear bottlenecks[C], in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2018, pp. 4510–4520. Wang, Wang, Zhang (b0205) 2021; 2021 Nguyen, Proença, Alonso-Fernandez (b0025) 2024; 56 M. Tan, Q. Le, EfficientNetV2: Smaller models and faster training[C], in: Proceedings of the 38th International Conference on Machine Learning (PMLR), 2021, pp. 10096–10106. Fathee, Sahmoud (b0020) 2021; 118 A.R. Kiruthiga, R. Arumuganathan, Smoothening of iris images and pupil segmentation using fractional derivative and wavelet transform[C], in: Proceedings of the 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN). 2017, pp. 1–6. Muhammad, Wang, Wang (b0165) 2023; 18 Z. Liu, Y. Wang, S. Vaidya, et al., KAN: Kolmogorov-arnold networks[J]. arXiv preprint arXiv:2404.19756, 2024. Wang, Xia, Yan (b0195) 2025; 175 Sun, Lu, Liu (b0155) 2022; 2022 Chen, Gan, Zeng (b0060) 2022; 37 Kong, Zhang, Wang (b0030) 2024; 51 Z. Liu, Y. Lin, Y. Cao, et al., Swin transformer: Hierarchical vision transformer using shifted windows[C], in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10012–10022. T. Chen, L. Zhu, C. Deng, et al., SAM-Adapter: Adapting segment anything in underperformed scenes[C], in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023) 3367–3375. Chen, Gan, Chen (b0065) 2023; 517 A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., An image is worth 16×16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020. Y. Yuan, K.W. Bowyer, A siamese network to detect if two iris images are monozygotic[J]. arXiv preprint arXiv:2503.09749, 2025. Jayadev, Bellary (b0175) 2024; 7 Chen, Zhu, Papandreou (b0120) 2018 O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation[C], Proceedings of the International Conference on Medical image computing and computer-assisted intervention, 2015, pp. 234–241. Guo, Chen, Xu (b0200) 2025; 113334 Wei, Wang, Gao (b0075) 2024; 19 Woo, Park, Lee (b0220) 2018 10.1016/j.displa.2025.103192_b0215 10.1016/j.displa.2025.103192_b0115 Wang (10.1016/j.displa.2025.103192_b0145) 2020; 15 10.1016/j.displa.2025.103192_b0035 Chen (10.1016/j.displa.2025.103192_b0060) 2022; 37 10.1016/j.displa.2025.103192_b0210 10.1016/j.displa.2025.103192_b0135 Pan (10.1016/j.displa.2025.103192_b0045) 2021; 33 Woo (10.1016/j.displa.2025.103192_b0220) 2018 Wang (10.1016/j.displa.2025.103192_b0205) 2021; 2021 10.1016/j.displa.2025.103192_b0090 Sun (10.1016/j.displa.2025.103192_b0005) 2021; 26 Sun (10.1016/j.displa.2025.103192_b0150) 2022; 31 Liu (10.1016/j.displa.2025.103192_b0160) 2023; 53 Samarin (10.1016/j.displa.2025.103192_b0185) 2024; 34 Sun (10.1016/j.displa.2025.103192_b0155) 2022; 2022 Wang (10.1016/j.displa.2025.103192_b0195) 2025; 175 Khaki (10.1016/j.displa.2025.103192_b0080) 2024; 24 Yan (10.1016/j.displa.2025.103192_b0170) 2024; 30 Yin (10.1016/j.displa.2025.103192_b0015) 2025 10.1016/j.displa.2025.103192_b0110 10.1016/j.displa.2025.103192_b0010 10.1016/j.displa.2025.103192_b0230 10.1016/j.displa.2025.103192_b0095 10.1016/j.displa.2025.103192_b0050 10.1016/j.displa.2025.103192_b0225 Wei (10.1016/j.displa.2025.103192_b0075) 2024; 19 He (10.1016/j.displa.2025.103192_b0190) 2025; 55 Chen (10.1016/j.displa.2025.103192_b0065) 2023; 517 Pourafkham (10.1016/j.displa.2025.103192_b0100) 2024 Jiang (10.1016/j.displa.2025.103192_b0130) 2025; 14 Jan (10.1016/j.displa.2025.103192_b0055) 2024; 83 Guo (10.1016/j.displa.2025.103192_b0200) 2025; 113334 Kong (10.1016/j.displa.2025.103192_b0030) 2024; 51 Fathee (10.1016/j.displa.2025.103192_b0020) 2021; 118 Chen (10.1016/j.displa.2025.103192_b0120) 2018 Jayadev (10.1016/j.displa.2025.103192_b0175) 2024; 7 Daugman (10.1016/j.displa.2025.103192_b0040) 2004; 14 10.1016/j.displa.2025.103192_b0085 10.1016/j.displa.2025.103192_b0140 Guo (10.1016/j.displa.2025.103192_b0070) 2022 Bonyani (10.1016/j.displa.2025.103192_b0105) 2024; 15 10.1016/j.displa.2025.103192_b0180 Avhad (10.1016/j.displa.2025.103192_b0125) 2025; 105 Muhammad (10.1016/j.displa.2025.103192_b0165) 2023; 18 Nguyen (10.1016/j.displa.2025.103192_b0025) 2024; 56 |
| References_xml | – reference: A. Dosovitskiy, L. Beyer, A. Kolesnikov, et al., An image is worth 16×16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929, 2020. – volume: 33 year: 2021 ident: b0045 article-title: Iris location method based on mathematical morphology and improved hough transform[J] publication-title: Biomed. Eng.: Appl., Basis Commun. – volume: 14 start-page: 246 year: 2025 ident: b0130 article-title: SAM-Iris: a SAM-based iris segmentation algorithm[J] publication-title: Electronics – reference: L. Yang, R.Y. Zhang, L. Li, et al., SimAM: A simple, parameter-free attention module for convolutional neural networks[C], in: Proceedings of the 38th International Conference on Machine Learning (PMLR). 2021, 139: 11863-11874. – volume: 56 start-page: 1 year: 2024 end-page: 35 ident: b0025 article-title: Deep learning for iris recognition: a survey[J] publication-title: ACM Comput. Surv. – reference: A.R. Kiruthiga, R. Arumuganathan, Smoothening of iris images and pupil segmentation using fractional derivative and wavelet transform[C], in: Proceedings of the 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN). 2017, pp. 1–6. – volume: 34 start-page: 855 year: 2024 end-page: 862 ident: b0185 article-title: Segmentation of the iris and pupil of the human eye in images from an infrared camera[J] publication-title: Pattern Recognit Image Anal. – volume: 53 start-page: 11267 year: 2023 end-page: 11281 ident: b0160 article-title: IrisST-Net for iris segmentation and contour parameters extraction[J] publication-title: Appl. Intell. – volume: 15 start-page: 2944 year: 2020 end-page: 2959 ident: b0145 article-title: Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition[J] publication-title: IEEE Trans. Inf. Forensics Secur. – volume: 7 start-page: 41 year: 2024 end-page: 54 ident: b0175 article-title: IrisSeg-drunk: enhanced iris segmentation and classification of drunk individuals using modified circle hough transform[J] publication-title: Iran J. Comput. Sci. – volume: 51 start-page: 186 year: 2024 end-page: 197 ident: b0030 article-title: Review of heterogeneous iris recognition[J] publication-title: Comput. Sci. – volume: 113334 year: 2025 ident: b0200 article-title: Multiscale attention feature deep fusion network for iris region localization and segmentation from dual-spectral iris image[J] publication-title: Appl. Soft Comput. – volume: 30 start-page: 85 year: 2024 ident: b0170 article-title: Iris-LAHNet: a lightweight attention-guided high-resolution network for iris segmentation and localization[J] publication-title: Multimedia Syst. – reference: T. Chen, L. Zhu, C. Deng, et al., SAM-Adapter: Adapting segment anything in underperformed scenes[C], in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023) 3367–3375. – reference: O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation[C], Proceedings of the International Conference on Medical image computing and computer-assisted intervention, 2015, pp. 234–241. – start-page: 1 year: 2024 end-page: 22 ident: b0100 article-title: ES-Net: UNet-based model for the semantic segmentation of iris[J] publication-title: Multimed. Tools Appl. – reference: Z. Liu, Y. Lin, Y. Cao, et al., Swin transformer: Hierarchical vision transformer using shifted windows[C], in: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 10012–10022. – reference: J. Hu, L. Shen, G. Sun, Squeeze‐and‐excitation networks[C], in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 7132–7141. – reference: K. He, X. Zhang, S. Ren, et al., Deep residual learning for image recognition[C], in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. – reference: S. Zhang, S. Zhang, Improved three-dimensional gaze estimation with precise iris segmentation based on YOLOv8[C], in: Proceedings of the 2024 36th Chinese Control and Decision Conference (CCDC), 2024, pp. 1970–1974. – volume: 517 start-page: 264 year: 2023 end-page: 278 ident: b0065 article-title: Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet[J] publication-title: Neurocomputing – reference: Y. Yuan, K.W. Bowyer, A siamese network to detect if two iris images are monozygotic[J]. arXiv preprint arXiv:2503.09749, 2025. – reference: M. Tan, Q. Le, EfficientNetV2: Smaller models and faster training[C], in: Proceedings of the 38th International Conference on Machine Learning (PMLR), 2021, pp. 10096–10106. – volume: 37 start-page: 829 year: 2022 end-page: 858 ident: b0060 article-title: DADCNet: dual attention densely connected network for more accurate real iris region segmentation[J] publication-title: Int. J.Intell. Syst. – start-page: 801 year: 2018: end-page: 818 ident: b0120 article-title: Encoder-decoder with atrous separable convolution for semantic image segmentation[C] publication-title: Proceedings of the European Conference on Computer Vision (ECCV) – volume: 31 year: 2022 ident: b0150 article-title: LiSeNet: Multitask lightweight segmentation network for accurate and complete iris segmentation[J] publication-title: J. Electron. Imaging – reference: Z. Liu, Y. Wang, S. Vaidya, et al., KAN: Kolmogorov-arnold networks[J]. arXiv preprint arXiv:2404.19756, 2024. – volume: 105 year: 2025 ident: b0125 article-title: An advanced deep learning model for iridology based disease diagnosis using pyramid network driven iris segmentation[J] publication-title: Biomed. Signal Process. Control – volume: 19 start-page: 6015 year: 2024 end-page: 6027 ident: b0075 article-title: Multi-faceted knowledge-driven graph neural network for iris segmentation[J] publication-title: IEEE Trans. Inf. Forensics Secur. – reference: A. Hossain, T. Sultan, S. Schuckers, Post-mortem human iris segmentation analysis with deep learning[J], 2024. arXiv preprint arXiv:2408.03448. – volume: 118 year: 2021 ident: b0020 article-title: Iris segmentation in uncooperative and unconstrained environments: state-of-the-art, datasets and future research directions[J] publication-title: Digital Signal Process. – start-page: 404 year: 2022: end-page: 413 ident: b0070 article-title: Attention skip connection dense network for accurate iris segmentation[C] publication-title: Proceedings of the Chinese Conference on Biometric Recognition (CCBR) – volume: 18 start-page: 2723 year: 2023 end-page: 2736 ident: b0165 article-title: IrisGuideNet: Guided localization and segmentation network for unconstrained iris biometrics[J] publication-title: IEEE Trans. Inf. Forensics Secur. – volume: 2021 start-page: 1 year: 2021 end-page: 10 ident: b0205 article-title: NIR iris challenge evaluation in non-cooperative environments: segmentation and localization[C] publication-title: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB). – volume: 24 year: 2024 ident: b0080 article-title: Robust convolutional neural network based on UNet for iris segmentation[J] publication-title: Int. J. Image Graph. – volume: 14 start-page: 21 year: 2004 end-page: 30 ident: b0040 article-title: How iris recogniton works[J] publication-title: IEEE Trans. Cricuits Syst. Video Technol. – volume: 15 start-page: 5239 year: 2024 end-page: 5255 ident: b0105 article-title: Different gaze direction (DGNet) collaborative learning for iris segmentation[J] publication-title: Int. J. Mach. Learn. Cybern. – volume: 83 start-page: 15223 year: 2024 end-page: 15251 ident: b0055 article-title: Iris segmentation for non-ideal iris biometric systems[J] publication-title: Multimed. Tools Appl. – volume: 55 start-page: 1 year: 2025 end-page: 18 ident: b0190 article-title: ISL-Net: dual-stream interaction network with task-optimized modules for more accurate, complete iris segmentation and localization[J] publication-title: Appl. Intell. – start-page: 1 year: 2025 end-page: 49 ident: b0015 article-title: Deep learning for iris recognition: a review[J] publication-title: Neural Comput. & Applic. – reference: M. Sandler, A. Howard, M. Zhu, et al., MobileNetV2: Inverted residuals and linear bottlenecks[C], in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), 2018, pp. 4510–4520. – volume: 175 year: 2025 ident: b0195 article-title: A light spatial-frequency network for robust iris segmentation and localization[J] publication-title: Appl. Soft Comput. – volume: 26 start-page: 1254 year: 2021 end-page: 1329 ident: b0005 article-title: Overview of biometrics research[J] publication-title: J. Image Graph. – reference: Z. Jia, J. Deng, L. Chi, et al., CondSeg: Ellipse estimation of pupil and iris via conditioned segmentation[J]. arXiv preprint arXiv:2408.17231, 2024. – volume: 2022 start-page: 1 year: 2022 end-page: 10 ident: b0155 article-title: Towards more accurate and complete iris segmentation using hybrid transformer U-Net[C]//Proceedings of the publication-title: IEEE Int. Joint Conf. Biometrics (IJCB). – start-page: 3 year: 2018: end-page: 19 ident: b0220 article-title: CBAM: Convolutional block attention module[C] publication-title: Proceedings of the European Conference on Computer Vision (ECCV) – volume: 53 start-page: 11267 issue: 9 year: 2023 ident: 10.1016/j.displa.2025.103192_b0160 article-title: IrisST-Net for iris segmentation and contour parameters extraction[J] publication-title: Appl. Intell. doi: 10.1007/s10489-022-03973-8 – ident: 10.1016/j.displa.2025.103192_b0215 doi: 10.1109/ICCV48922.2021.00986 – volume: 26 start-page: 1254 issue: 6 year: 2021 ident: 10.1016/j.displa.2025.103192_b0005 article-title: Overview of biometrics research[J] publication-title: J. Image Graph. doi: 10.11834/jig.210078 – volume: 14 start-page: 246 issue: 2 year: 2025 ident: 10.1016/j.displa.2025.103192_b0130 article-title: SAM-Iris: a SAM-based iris segmentation algorithm[J] publication-title: Electronics doi: 10.3390/electronics14020246 – ident: 10.1016/j.displa.2025.103192_b0140 doi: 10.1109/ICCVW60793.2023.00361 – volume: 7 start-page: 41 issue: 1 year: 2024 ident: 10.1016/j.displa.2025.103192_b0175 article-title: IrisSeg-drunk: enhanced iris segmentation and classification of drunk individuals using modified circle hough transform[J] publication-title: Iran J. Comput. Sci. doi: 10.1007/s42044-023-00157-6 – volume: 15 start-page: 5239 issue: 11 year: 2024 ident: 10.1016/j.displa.2025.103192_b0105 article-title: Different gaze direction (DGNet) collaborative learning for iris segmentation[J] publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-024-02232-1 – volume: 517 start-page: 264 year: 2023 ident: 10.1016/j.displa.2025.103192_b0065 article-title: Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet[J] publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.10.064 – start-page: 801 year: 2018 ident: 10.1016/j.displa.2025.103192_b0120 article-title: Encoder-decoder with atrous separable convolution for semantic image segmentation[C] publication-title: Proceedings of the European Conference on Computer Vision (ECCV) – volume: 37 start-page: 829 issue: 1 year: 2022 ident: 10.1016/j.displa.2025.103192_b0060 article-title: DADCNet: dual attention densely connected network for more accurate real iris region segmentation[J] publication-title: Int. J.Intell. Syst. doi: 10.1002/int.22649 – volume: 30 start-page: 85 issue: 2 year: 2024 ident: 10.1016/j.displa.2025.103192_b0170 article-title: Iris-LAHNet: a lightweight attention-guided high-resolution network for iris segmentation and localization[J] publication-title: Multimedia Syst. doi: 10.1007/s00530-024-01280-5 – ident: 10.1016/j.displa.2025.103192_b0095 doi: 10.1109/CCDC62350.2024.10588166 – ident: 10.1016/j.displa.2025.103192_b0210 – volume: 113334 year: 2025 ident: 10.1016/j.displa.2025.103192_b0200 article-title: Multiscale attention feature deep fusion network for iris region localization and segmentation from dual-spectral iris image[J] publication-title: Appl. Soft Comput. – start-page: 404 year: 2022 ident: 10.1016/j.displa.2025.103192_b0070 article-title: Attention skip connection dense network for accurate iris segmentation[C] publication-title: Proceedings of the Chinese Conference on Biometric Recognition (CCBR) doi: 10.1007/978-3-031-20233-9_41 – volume: 55 start-page: 1 issue: 6 year: 2025 ident: 10.1016/j.displa.2025.103192_b0190 article-title: ISL-Net: dual-stream interaction network with task-optimized modules for more accurate, complete iris segmentation and localization[J] publication-title: Appl. Intell. – volume: 33 issue: 01 year: 2021 ident: 10.1016/j.displa.2025.103192_b0045 article-title: Iris location method based on mathematical morphology and improved hough transform[J] publication-title: Biomed. Eng.: Appl., Basis Commun. – volume: 83 start-page: 15223 issue: 5 year: 2024 ident: 10.1016/j.displa.2025.103192_b0055 article-title: Iris segmentation for non-ideal iris biometric systems[J] publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-021-11075-9 – ident: 10.1016/j.displa.2025.103192_b0225 – ident: 10.1016/j.displa.2025.103192_b0085 doi: 10.1007/978-3-319-24574-4_28 – volume: 2021 start-page: 1 year: 2021 ident: 10.1016/j.displa.2025.103192_b0205 article-title: NIR iris challenge evaluation in non-cooperative environments: segmentation and localization[C] publication-title: Proceedings of the IEEE International Joint Conference on Biometrics (IJCB). – volume: 24 issue: 04 year: 2024 ident: 10.1016/j.displa.2025.103192_b0080 article-title: Robust convolutional neural network based on UNet for iris segmentation[J] publication-title: Int. J. Image Graph. doi: 10.1142/S0219467824500426 – volume: 105 year: 2025 ident: 10.1016/j.displa.2025.103192_b0125 article-title: An advanced deep learning model for iridology based disease diagnosis using pyramid network driven iris segmentation[J] publication-title: Biomed. Signal Process. Control doi: 10.1016/j.bspc.2025.107595 – ident: 10.1016/j.displa.2025.103192_b0110 – volume: 15 start-page: 2944 year: 2020 ident: 10.1016/j.displa.2025.103192_b0145 article-title: Towards complete and accurate iris segmentation using deep multi-task attention network for non-cooperative iris recognition[J] publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2020.2980791 – start-page: 3 year: 2018 ident: 10.1016/j.displa.2025.103192_b0220 article-title: CBAM: Convolutional block attention module[C] publication-title: Proceedings of the European Conference on Computer Vision (ECCV) – volume: 118 year: 2021 ident: 10.1016/j.displa.2025.103192_b0020 article-title: Iris segmentation in uncooperative and unconstrained environments: state-of-the-art, datasets and future research directions[J] publication-title: Digital Signal Process. doi: 10.1016/j.dsp.2021.103244 – ident: 10.1016/j.displa.2025.103192_b0050 doi: 10.1109/ICSCN.2017.8085713 – ident: 10.1016/j.displa.2025.103192_b0135 – start-page: 1 year: 2025 ident: 10.1016/j.displa.2025.103192_b0015 article-title: Deep learning for iris recognition: a review[J] publication-title: Neural Comput. & Applic. – volume: 31 issue: 5 year: 2022 ident: 10.1016/j.displa.2025.103192_b0150 article-title: LiSeNet: Multitask lightweight segmentation network for accurate and complete iris segmentation[J] publication-title: J. Electron. Imaging doi: 10.1117/1.JEI.31.5.053035 – ident: 10.1016/j.displa.2025.103192_b0180 doi: 10.1007/978-3-031-91989-3_12 – volume: 18 start-page: 2723 year: 2023 ident: 10.1016/j.displa.2025.103192_b0165 article-title: IrisGuideNet: Guided localization and segmentation network for unconstrained iris biometrics[J] publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2023.3268504 – volume: 19 start-page: 6015 year: 2024 ident: 10.1016/j.displa.2025.103192_b0075 article-title: Multi-faceted knowledge-driven graph neural network for iris segmentation[J] publication-title: IEEE Trans. Inf. Forensics Secur. doi: 10.1109/TIFS.2024.3407508 – ident: 10.1016/j.displa.2025.103192_b0115 doi: 10.1109/CVPR.2018.00474 – ident: 10.1016/j.displa.2025.103192_b0230 doi: 10.1109/CVPR.2018.00745 – ident: 10.1016/j.displa.2025.103192_b0090 doi: 10.1109/CVPR.2016.90 – ident: 10.1016/j.displa.2025.103192_b0010 – volume: 56 start-page: 1 issue: 9 year: 2024 ident: 10.1016/j.displa.2025.103192_b0025 article-title: Deep learning for iris recognition: a survey[J] publication-title: ACM Comput. Surv. doi: 10.1145/3651306 – start-page: 1 year: 2024 ident: 10.1016/j.displa.2025.103192_b0100 article-title: ES-Net: UNet-based model for the semantic segmentation of iris[J] publication-title: Multimed. Tools Appl. – ident: 10.1016/j.displa.2025.103192_b0035 – volume: 34 start-page: 855 issue: 3 year: 2024 ident: 10.1016/j.displa.2025.103192_b0185 article-title: Segmentation of the iris and pupil of the human eye in images from an infrared camera[J] publication-title: Pattern Recognit Image Anal. doi: 10.1134/S1054661824700743 – volume: 175 year: 2025 ident: 10.1016/j.displa.2025.103192_b0195 article-title: A light spatial-frequency network for robust iris segmentation and localization[J] publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2025.113009 – volume: 14 start-page: 21 issue: 1 year: 2004 ident: 10.1016/j.displa.2025.103192_b0040 article-title: How iris recogniton works[J] publication-title: IEEE Trans. Cricuits Syst. Video Technol. doi: 10.1109/TCSVT.2003.818350 – volume: 51 start-page: 186 issue: 06 year: 2024 ident: 10.1016/j.displa.2025.103192_b0030 article-title: Review of heterogeneous iris recognition[J] publication-title: Comput. Sci. – volume: 2022 start-page: 1 year: 2022 ident: 10.1016/j.displa.2025.103192_b0155 article-title: Towards more accurate and complete iris segmentation using hybrid transformer U-Net[C]//Proceedings of the publication-title: IEEE Int. Joint Conf. Biometrics (IJCB). |
| SSID | ssj0002472 |
| Score | 2.4072304 |
| Snippet | •Proposed a cascade attention feature residual fusion network (CA-RFNet).•Deep convolutional residual blocks in the encoder to enhance feature representation... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 103192 |
| SubjectTerms | Boundary perception module CA-RFNet Cascade attention fusion module Deep convolutional residual block Iris localization and segmentation |
| Title | Cascade attention feature residual fusion network for iris localization and segmentation in non-cooperative environments |
| URI | https://dx.doi.org/10.1016/j.displa.2025.103192 |
| Volume | 91 |
| WOSCitedRecordID | wos001563420500001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: ScienceDirect (Freedom Collection) issn: 0141-9382 databaseCode: AIEXJ dateStart: 19950101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.sciencedirect.com omitProxy: false ssIdentifier: ssj0002472 providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLfKxgEOaHyJAUM-cKsyNZnt2MepjG0ITUgMqZwix7EhW0mrtZnKlb-c5680ZWhiBy5RZDmO4_fze88v7wOhtyNRKipK2Eg5FQnI6zThFZxatSpHqRacEO2SuH7Mz874ZCI-DQa_YizM9TRvGr5aifl_JTW0AbFt6OwdyN0NCg1wD0SHK5Adrv9E-LFcWJ_3oU2c6V0ZjXbZO4dwsvahV6a1NrJh413AnadhDZt96ARbCMx0fxUW-tuPEJzkPCKbWZOo2WyuQ77wfphcX819Vy_mU_mz09ePW2eR_fy9LesOjeMQGPI1Sk-rT-vgIdw2l3LdHLuetPU09g6miqxvqojWyzQRB3yD_fpiXYF_2qITvjbeDdburQwX-5X7AjjZZ3R_3X0zk_YfEq7zO4wubReFH6WwoxR-lHtoOwO8AmfcPjw9mnzo5HlGXAmwbvIxANN5Cd6czd8VnJ7Scr6DHoXTBj70KHmMBrp5gh72clA-RauAF9zhBQe84IgX7PGCA14w4AVbvOA-XjDgBffxgmt4YhMvuI-XZ-jL-6Pz8UkSynEkCvj6MiGMUamZ4VXFGUmNDVIWMsvVSDNuYMvTihOuOKyTkVLprCSCUJAIjEhBS3rwHG3Ba_ULhDPGaU40SbUhhCgmDWGi1Cw1FSiwcrSLkriGxdxnXSluo90uyuNCF0Fz9BphAei59cmXd3zTK_RgjezXaGt51eo9dF9dL-vF1ZsAnd_cnpaN |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Cascade+attention+feature+residual+fusion+network+for+iris+localization+and+segmentation+in+non-cooperative+environments&rft.jtitle=Displays&rft.au=Guo%2C+Shubin&rft.au=Chen%2C+Ying&rft.au=Deng%2C+Junkang&rft.au=Chen%2C+Huiling&rft.date=2026-01-01&rft.issn=0141-9382&rft.volume=91&rft.spage=103192&rft_id=info:doi/10.1016%2Fj.displa.2025.103192&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_displa_2025_103192 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0141-9382&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0141-9382&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0141-9382&client=summon |