{ "ok": true, "doi": "10.46243/jst.2023.v8.i12.pp147-155", "events": [ { "seq": 1, "kind": "register", "at": "2026-09-29 22:00:28", "by": "Administrator (admin)", "by_kind": "user", "note": "registered at Crossref; record read from api.crossref.org", "changes": [ { "path": "abstract.lang", "from": null, "to": "en" }, { "path": "abstract.value", "from": null, "to": "The acceleration of the emergence of modern technological resources in recent years has given rise to a need for accurate user recognition systems to restrict access to the technologies. The biometric recognition systems are the most powerful option to date. Biometrics is the science of establishing the identity of a person through semi or fully automated techniques based on behavioural traits, such as voice or signature, and/or physical traits, such as the iris and the fingerprint. The unique nature of biometrical data gives it many advantages over traditional recognition methods, such as passwords, as it cannot be lost, stolen, or replicated. Biometric traits can be categorized into two groups: extrinsic biometric traits such as iris and fingerprint, and intrinsic biometric traits such as palm. Extrinsic traits are visible and can be affected by external factors, while the intrinsic features cannot be affected by external factors. In general, the biometric recognition system consists of four modules: sensor, feature extraction, matching, and decision-making modules. There are two types of biometric recognition systems, unimodal and multimodal. The unimodal system uses a single biometric trait to recognize the user. While unimodal systems are trustworthy and have proven superior to previously used traditional methods, but they have limitations. These include problems with noise in the sensed data, non-universality problems, vulnerability to spoofing attacks, intra-class, and inter-class similarity. Basically, multimodal biometric systems require more than one trait to recognize users. They have been widely applied in real-world applications due to their ability to overcome the problems encountered by unimodal biometric systems. In multimodal biometric systems, the different traits can be fused using the available information in one of the biometric system’s modules. The advantages of multimodal biometric systems over unimodal systems have made them a very attractive secure recognition method.Therefore, with the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome several significant limitations of unimodal biometric systems. In this project, an enhanced multi-modal biometric authentication system is presented using modified deep learning model to authenticate persons using different biometric features such as Face, Iris, Finger, Palm and Ear" }, { "path": "agents.0.name.family", "from": null, "to": "S. Venkata Ramana" }, { "path": "agents.0.name.given", "from": null, "to": "S. Venkata Ramana" }, { "path": "agents.0.role", "from": null, "to": "author" }, { "path": "agents.0.sequence", "from": null, "to": "first" }, { "path": "agents.1.name.org", "from": null, "to": "Longman Publishers" }, { "path": "agents.1.role", "from": null, "to": "publisher" }, { "path": "characters.0", "from": null, "to": "Language" }, { "path": "container.identifiers.0.medium", "from": null, "to": "electronic" }, { "path": "container.identifiers.0.type", "from": null, "to": "ISSN" }, { "path": "container.identifiers.0.value", "from": null, "to": "2456-5660" }, { "path": "container.issue", "from": null, "to": "12" }, { "path": "container.pages.first", "from": null, "to": "147" }, { "path": "container.pages.last", "from": null, "to": "115" }, { "path": "container.titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "container.titles.0.value", "from": null, "to": "Journal of Science & Technology" }, { "path": "container.type", "from": null, "to": "Journal" }, { "path": "container.volume", "from": null, "to": "8" }, { "path": "dates.date_type", "from": null, "to": "PublicationDate" }, { "path": "dates.online", "from": null, "to": "2023-12-12" }, { "path": "dates.published", "from": null, "to": "2023-12-12" }, { "path": "doi", "from": null, "to": "10.46243/jst.2023.v8.i12.pp147-155" }, { "path": "format", "from": null, "to": "smartscholars-doi-metadata/1.0" }, { "path": "identifiers.0.type", "from": null, "to": "DOI" }, { "path": "identifiers.0.value", "from": null, "to": "10.46243/jst.2023.v8.i12.pp147-155" }, { "path": "language", "from": null, "to": "en" }, { "path": "license.applies_to", "from": null, "to": "vor" }, { "path": "license.start", "from": null, "to": "2023-12-12" }, { "path": "license.url", "from": null, "to": "https://creativecommons.org/licenses/by/4.0/" }, { "path": "links.0.primary", "from": null, "to": true }, { "path": "links.0.return_type", "from": null, "to": "text/html" }, { "path": "links.0.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/view/878" }, { "path": "links.1.purpose", "from": null, "to": "text-mining" }, { "path": "links.1.return_type", "from": null, "to": "application/pdf" }, { "path": "links.1.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/878/805" }, { "path": "links.2.purpose", "from": null, "to": "text-mining" }, { "path": "links.2.return_type", "from": null, "to": "application/xml" }, { "path": "links.2.url", "from": null, "to": "https://www.jst.org.in/index.php/pub/article/download/878/1682" }, { "path": "links.3.purpose", "from": null, "to": "similarity-checking" }, { "path": "links.3.url", "from": null, "to": "https://jst.org.in/admin/uploads/A8-Multimodel%20Authentication.pdf" }, { "path": "modes.0", "from": null, "to": "Visual" }, { "path": "record.issue_number", "from": null, "to": 1 }, { "path": "record.registered", "from": null, "to": "2024-02-16" }, { "path": "record.registrant", "from": null, "to": "Longman Publishers" }, { "path": "record.source", "from": null, "to": "crossref-api" }, { "path": "record.source_agency", "from": null, "to": "Crossref (member 25296)" }, { "path": "record.updated", "from": null, "to": "2026-09-17" }, { "path": "references.0.doi", "from": null, "to": "10.1109/access.2021.3061589" }, { "path": "references.0.key", "from": null, "to": "ref1" }, { "path": "references.0.unstructured", "from": null, "to": "R. Ryu, S. Yeom, S. -H. Kim and D. Herbert, “Continuous Multimodal Biometric Authentication Schemes: A Systematic Review,” in IEEE Access, vol. 9, pp. 34541-34557, 2021, doi: 10.1109/ACCESS.2021.3061589" }, { "path": "references.1.doi", "from": null, "to": "10.1109/access.2018.2886573" }, { "path": "references.1.key", "from": null, "to": "ref2" }, { "path": "references.1.unstructured", "from": null, "to": "M. Hammad, Y. Liu and K. Wang, “Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint,” in IEEE Access, vol. 7, pp. 26527-26542, 2019, doi: 10.1109/ACCESS.2018.2886573" }, { "path": "references.10.doi", "from": null, "to": "10.3390/fi14080222" }, { "path": "references.10.key", "from": null, "to": "ref11" }, { "path": "references.10.unstructured", "from": null, "to": "Ahamed F, Farid F, Suleiman B, Jan Z, Wahsheh LA, Shahrestani S. An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services. Future Internet. 2022; 14(8):222. https://doi.org/10.3390/ fi14080222" }, { "path": "references.2.doi", "from": null, "to": "10.1109/esci48226.2020.9167512" }, { "path": "references.2.key", "from": null, "to": "ref3" }, { "path": "references.2.unstructured", "from": null, "to": "S. S. Sengar, U. Hariharan and K. Rajkumar, “Multimodal Biometric Authentication System using Deep Learning Method,” 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020, pp. 309-312, doi: 10.1109/ESCI48226.2020.9167512" }, { "path": "references.3.doi", "from": null, "to": "10.1007/s12652-020-02184-8" }, { "path": "references.3.key", "from": null, "to": "ref4" }, { "path": "references.3.unstructured", "from": null, "to": "Joseph, T., Kalaiselvan, S.A., Aswathy, S.U. et al. RETRACTED ARTICLE: A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment. J Ambient Intell Human Comput 12, 6141–6149 (2021). https://doi.org/10.1007/s12652-020- 02184-8" }, { "path": "references.4.doi", "from": null, "to": "10.1109/icoei.2019.8862563" }, { "path": "references.4.key", "from": null, "to": "ref5" }, { "path": "references.4.unstructured", "from": null, "to": "S. K. Choudhary and A. K. Naik, “Multimodal Biometric Authentication with Secured Templates—A Review,” 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019, pp. 1062-1069, doi: 10.1109/ICOEI.2019.8862563" }, { "path": "references.5.doi", "from": null, "to": "10.1109/tifs.2019.2944058" }, { "path": "references.5.key", "from": null, "to": "ref6" }, { "path": "references.5.unstructured", "from": null, "to": "L. Wu, J. Yang, M. Zhou, Y. Chen and Q. Wang, “LVID: A Multimodal Biometrics Authentication System on Smartphones,” in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1572-1585, 2020, doi: 10.1109/ TIFS.2019.2944058" }, { "path": "references.6.key", "from": null, "to": "ref7" }, { "path": "references.6.unstructured", "from": null, "to": "X. Zhang, L. Yao, C. Huang, T. Gu, Z. Yang, and The below figure shows the authentication of multi model images of face, eyes, ears, palm, fingerprint as id 4 Figure 4: Displays the Accuracy and Loss Comparison graph for the Deep learning Multimodal. Figure 4: Displays the uploading sample1 testing folder. Figure 5: Displays the authentication of multi model as id" }, { "path": "references.7.doi", "from": null, "to": "10.1145/3393619" }, { "path": "references.7.key", "from": null, "to": "ref8" }, { "path": "references.7.unstructured", "from": null, "to": "Figure 6: Displays the authentication of multi model as id 9 CONCLUSION AND FUTURE WORK With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our S. Venkata Ramana, A. Shirisha, B. Spandhana, Ch. Vandhana: AN ENHANCED MUL TI-MODAL BIOMETRIC AUTHENTICATION SYSTEM USING MODIFIED DEEP LEARNING MODEL Y. Liu. 2020. DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves. ACM Trans. Intell. Syst. Technol. 11, 4, Article 49 (August 2020), 24 pages. https://doi.org/10.1145/3393619" }, { "path": "references.8.doi", "from": null, "to": "10.1007/s00530-021-00810-9" }, { "path": "references.8.key", "from": null, "to": "ref9" }, { "path": "references.8.unstructured", "from": null, "to": "Rahiem, B.A., El-Samie, F.E.A. & Amin, M. Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein. Multimedia Systems 28, 1325–1337 (2022). https://doi.org/10.1007/s00530-021- 00810-9" }, { "path": "references.9.doi", "from": null, "to": "10.1109/access.2020.2999115" }, { "path": "references.9.key", "from": null, "to": "ref10" }, { "path": "references.9.unstructured", "from": null, "to": "X. Zhang, D. Cheng, P. Jia, Y. Dai and X. Xu, “An Efficient Android-Based Multimodal Biometric Authentication System With Face and Voice,” in IEEE Access, vol. 8, pp. 102757-102772, 2020, doi: 10.1109/ACCESS.2020.2999115" }, { "path": "referent", "from": null, "to": "Creation" }, { "path": "structural_type", "from": null, "to": "Digital" }, { "path": "titles.0.lang", "from": null, "to": "en" }, { "path": "titles.0.type", "from": null, "to": "PrincipalTitle" }, { "path": "titles.0.value", "from": null, "to": "AN ENHANCED MULTI-MODAL BIOMETRIC AUTHENTICATION SYSTEM USING MODIFIED DEEP LEARNING MODEL" }, { "path": "type", "from": null, "to": "JournalArticle" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i12.pp147-155", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "AN ENHANCED MULTI-MODAL BIOMETRIC AUTHENTICATION SYSTEM USING MODIFIED DEEP LEARNING MODEL", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i12.pp147-155" } ], "agents": [ { "role": "author", "name": { "given": "S. Venkata Ramana", "family": "S. Venkata Ramana" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-12-12", "date_type": "PublicationDate", "online": "2023-12-12" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "12", "pages": { "first": "147", "last": "115" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/878", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/878/805", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/878/1682", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/A8-Multimodel%20Authentication.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "The acceleration of the emergence of modern technological resources in recent years has given rise to a need for accurate user recognition systems to restrict access to the technologies. The biometric recognition systems are the most powerful option to date. Biometrics is the science of establishing the identity of a person through semi or fully automated techniques based on behavioural traits, such as voice or signature, and/or physical traits, such as the iris and the fingerprint. The unique nature of biometrical data gives it many advantages over traditional recognition methods, such as passwords, as it cannot be lost, stolen, or replicated. Biometric traits can be categorized into two groups: extrinsic biometric traits such as iris and fingerprint, and intrinsic biometric traits such as palm. Extrinsic traits are visible and can be affected by external factors, while the intrinsic features cannot be affected by external factors. In general, the biometric recognition system consists of four modules: sensor, feature extraction, matching, and decision-making modules. There are two types of biometric recognition systems, unimodal and multimodal. The unimodal system uses a single biometric trait to recognize the user. While unimodal systems are trustworthy and have proven superior to previously used traditional methods, but they have limitations. These include problems with noise in the sensed data, non-universality problems, vulnerability to spoofing attacks, intra-class, and inter-class similarity. Basically, multimodal biometric systems require more than one trait to recognize users. They have been widely applied in real-world applications due to their ability to overcome the problems encountered by unimodal biometric systems. In multimodal biometric systems, the different traits can be fused using the available information in one of the biometric system’s modules. The advantages of multimodal biometric systems over unimodal systems have made them a very attractive secure recognition method.Therefore, with the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome several significant limitations of unimodal biometric systems. In this project, an enhanced multi-modal biometric authentication system is presented using modified deep learning model to authenticate persons using different biometric features such as Face, Iris, Finger, Palm and Ear", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-12-12", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/access.2021.3061589", "unstructured": "R. Ryu, S. Yeom, S. -H. Kim and D. Herbert, “Continuous Multimodal Biometric Authentication Schemes: A Systematic Review,” in IEEE Access, vol. 9, pp. 34541-34557, 2021, doi: 10.1109/ACCESS.2021.3061589" }, { "key": "ref2", "doi": "10.1109/access.2018.2886573", "unstructured": "M. Hammad, Y. Liu and K. Wang, “Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint,” in IEEE Access, vol. 7, pp. 26527-26542, 2019, doi: 10.1109/ACCESS.2018.2886573" }, { "key": "ref3", "doi": "10.1109/esci48226.2020.9167512", "unstructured": "S. S. Sengar, U. Hariharan and K. Rajkumar, “Multimodal Biometric Authentication System using Deep Learning Method,” 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020, pp. 309-312, doi: 10.1109/ESCI48226.2020.9167512" }, { "key": "ref4", "doi": "10.1007/s12652-020-02184-8", "unstructured": "Joseph, T., Kalaiselvan, S.A., Aswathy, S.U. et al. RETRACTED ARTICLE: A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment. J Ambient Intell Human Comput 12, 6141–6149 (2021). https://doi.org/10.1007/s12652-020- 02184-8" }, { "key": "ref5", "doi": "10.1109/icoei.2019.8862563", "unstructured": "S. K. Choudhary and A. K. Naik, “Multimodal Biometric Authentication with Secured Templates—A Review,” 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019, pp. 1062-1069, doi: 10.1109/ICOEI.2019.8862563" }, { "key": "ref6", "doi": "10.1109/tifs.2019.2944058", "unstructured": "L. Wu, J. Yang, M. Zhou, Y. Chen and Q. Wang, “LVID: A Multimodal Biometrics Authentication System on Smartphones,” in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1572-1585, 2020, doi: 10.1109/ TIFS.2019.2944058" }, { "key": "ref7", "unstructured": "X. Zhang, L. Yao, C. Huang, T. Gu, Z. Yang, and The below figure shows the authentication of multi model images of face, eyes, ears, palm, fingerprint as id 4 Figure 4: Displays the Accuracy and Loss Comparison graph for the Deep learning Multimodal. Figure 4: Displays the uploading sample1 testing folder. Figure 5: Displays the authentication of multi model as id" }, { "key": "ref8", "doi": "10.1145/3393619", "unstructured": "Figure 6: Displays the authentication of multi model as id 9 CONCLUSION AND FUTURE WORK With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our S. Venkata Ramana, A. Shirisha, B. Spandhana, Ch. Vandhana: AN ENHANCED MUL TI-MODAL BIOMETRIC AUTHENTICATION SYSTEM USING MODIFIED DEEP LEARNING MODEL Y. Liu. 2020. DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves. ACM Trans. Intell. Syst. Technol. 11, 4, Article 49 (August 2020), 24 pages. https://doi.org/10.1145/3393619" }, { "key": "ref9", "doi": "10.1007/s00530-021-00810-9", "unstructured": "Rahiem, B.A., El-Samie, F.E.A. & Amin, M. Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein. Multimedia Systems 28, 1325–1337 (2022). https://doi.org/10.1007/s00530-021- 00810-9" }, { "key": "ref10", "doi": "10.1109/access.2020.2999115", "unstructured": "X. Zhang, D. Cheng, P. Jia, Y. Dai and X. Xu, “An Efficient Android-Based Multimodal Biometric Authentication System With Face and Voice,” in IEEE Access, vol. 8, pp. 102757-102772, 2020, doi: 10.1109/ACCESS.2020.2999115" }, { "key": "ref11", "doi": "10.3390/fi14080222", "unstructured": "Ahamed F, Farid F, Suleiman B, Jan Z, Wahsheh LA, Shahrestani S. An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services. Future Internet. 2022; 14(8):222. https://doi.org/10.3390/ fi14080222" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/878", "sha256_before": null, "sha256_after": "f409d3cf39ff0e49183ccc0c9682e8701537b4a32badc0e5b2ce202001d88229" }, { "seq": 2, "kind": "update", "at": "2026-09-29 23:59:47", "by": "Administrator (admin)", "by_kind": "user", "note": "record re-read from api.crossref.org", "changes": [ { "path": "container.titles.0.value", "from": "Journal of Science & Technology", "to": "Journal of Science & Technology" } ], "record_after": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i12.pp147-155", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "AN ENHANCED MULTI-MODAL BIOMETRIC AUTHENTICATION SYSTEM USING MODIFIED DEEP LEARNING MODEL", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i12.pp147-155" } ], "agents": [ { "role": "author", "name": { "given": "S. Venkata Ramana", "family": "S. Venkata Ramana" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-12-12", "date_type": "PublicationDate", "online": "2023-12-12" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "12", "pages": { "first": "147", "last": "115" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/878", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/878/805", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/878/1682", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/A8-Multimodel%20Authentication.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "The acceleration of the emergence of modern technological resources in recent years has given rise to a need for accurate user recognition systems to restrict access to the technologies. The biometric recognition systems are the most powerful option to date. Biometrics is the science of establishing the identity of a person through semi or fully automated techniques based on behavioural traits, such as voice or signature, and/or physical traits, such as the iris and the fingerprint. The unique nature of biometrical data gives it many advantages over traditional recognition methods, such as passwords, as it cannot be lost, stolen, or replicated. Biometric traits can be categorized into two groups: extrinsic biometric traits such as iris and fingerprint, and intrinsic biometric traits such as palm. Extrinsic traits are visible and can be affected by external factors, while the intrinsic features cannot be affected by external factors. In general, the biometric recognition system consists of four modules: sensor, feature extraction, matching, and decision-making modules. There are two types of biometric recognition systems, unimodal and multimodal. The unimodal system uses a single biometric trait to recognize the user. While unimodal systems are trustworthy and have proven superior to previously used traditional methods, but they have limitations. These include problems with noise in the sensed data, non-universality problems, vulnerability to spoofing attacks, intra-class, and inter-class similarity. Basically, multimodal biometric systems require more than one trait to recognize users. They have been widely applied in real-world applications due to their ability to overcome the problems encountered by unimodal biometric systems. In multimodal biometric systems, the different traits can be fused using the available information in one of the biometric system’s modules. The advantages of multimodal biometric systems over unimodal systems have made them a very attractive secure recognition method.Therefore, with the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome several significant limitations of unimodal biometric systems. In this project, an enhanced multi-modal biometric authentication system is presented using modified deep learning model to authenticate persons using different biometric features such as Face, Iris, Finger, Palm and Ear", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-12-12", "applies_to": "vor" }, "references": [ { "key": "ref1", "doi": "10.1109/access.2021.3061589", "unstructured": "R. Ryu, S. Yeom, S. -H. Kim and D. Herbert, “Continuous Multimodal Biometric Authentication Schemes: A Systematic Review,” in IEEE Access, vol. 9, pp. 34541-34557, 2021, doi: 10.1109/ACCESS.2021.3061589" }, { "key": "ref2", "doi": "10.1109/access.2018.2886573", "unstructured": "M. Hammad, Y. Liu and K. Wang, “Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint,” in IEEE Access, vol. 7, pp. 26527-26542, 2019, doi: 10.1109/ACCESS.2018.2886573" }, { "key": "ref3", "doi": "10.1109/esci48226.2020.9167512", "unstructured": "S. S. Sengar, U. Hariharan and K. Rajkumar, “Multimodal Biometric Authentication System using Deep Learning Method,” 2020 International Conference on Emerging Smart Computing and Informatics (ESCI), 2020, pp. 309-312, doi: 10.1109/ESCI48226.2020.9167512" }, { "key": "ref4", "doi": "10.1007/s12652-020-02184-8", "unstructured": "Joseph, T., Kalaiselvan, S.A., Aswathy, S.U. et al. RETRACTED ARTICLE: A multimodal biometric authentication scheme based on feature fusion for improving security in cloud environment. J Ambient Intell Human Comput 12, 6141–6149 (2021). https://doi.org/10.1007/s12652-020- 02184-8" }, { "key": "ref5", "doi": "10.1109/icoei.2019.8862563", "unstructured": "S. K. Choudhary and A. K. Naik, “Multimodal Biometric Authentication with Secured Templates—A Review,” 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), 2019, pp. 1062-1069, doi: 10.1109/ICOEI.2019.8862563" }, { "key": "ref6", "doi": "10.1109/tifs.2019.2944058", "unstructured": "L. Wu, J. Yang, M. Zhou, Y. Chen and Q. Wang, “LVID: A Multimodal Biometrics Authentication System on Smartphones,” in IEEE Transactions on Information Forensics and Security, vol. 15, pp. 1572-1585, 2020, doi: 10.1109/ TIFS.2019.2944058" }, { "key": "ref7", "unstructured": "X. Zhang, L. Yao, C. Huang, T. Gu, Z. Yang, and The below figure shows the authentication of multi model images of face, eyes, ears, palm, fingerprint as id 4 Figure 4: Displays the Accuracy and Loss Comparison graph for the Deep learning Multimodal. Figure 4: Displays the uploading sample1 testing folder. Figure 5: Displays the authentication of multi model as id" }, { "key": "ref8", "doi": "10.1145/3393619", "unstructured": "Figure 6: Displays the authentication of multi model as id 9 CONCLUSION AND FUTURE WORK With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our S. Venkata Ramana, A. Shirisha, B. Spandhana, Ch. Vandhana: AN ENHANCED MUL TI-MODAL BIOMETRIC AUTHENTICATION SYSTEM USING MODIFIED DEEP LEARNING MODEL Y. Liu. 2020. DeepKey: A Multimodal Biometric Authentication System via Deep Decoding Gaits and Brainwaves. ACM Trans. Intell. Syst. Technol. 11, 4, Article 49 (August 2020), 24 pages. https://doi.org/10.1145/3393619" }, { "key": "ref9", "doi": "10.1007/s00530-021-00810-9", "unstructured": "Rahiem, B.A., El-Samie, F.E.A. & Amin, M. Multimodal biometric authentication based on deep fusion of electrocardiogram (ECG) and finger vein. Multimedia Systems 28, 1325–1337 (2022). https://doi.org/10.1007/s00530-021- 00810-9" }, { "key": "ref10", "doi": "10.1109/access.2020.2999115", "unstructured": "X. Zhang, D. Cheng, P. Jia, Y. Dai and X. Xu, “An Efficient Android-Based Multimodal Biometric Authentication System With Face and Voice,” in IEEE Access, vol. 8, pp. 102757-102772, 2020, doi: 10.1109/ACCESS.2020.2999115" }, { "key": "ref11", "doi": "10.3390/fi14080222", "unstructured": "Ahamed F, Farid F, Suleiman B, Jan Z, Wahsheh LA, Shahrestani S. An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services. Future Internet. 2022; 14(8):222. https://doi.org/10.3390/ fi14080222" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "url_after": "https://www.jst.org.in/index.php/pub/article/view/878", "sha256_before": "f409d3cf39ff0e49183ccc0c9682e8701537b4a32badc0e5b2ce202001d88229", "sha256_after": "2e0870c6ea903f4ba39b5ebb7c0d548680d60474feddc22aed890ffaceb06439" } ] }