{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T18:14:40Z","timestamp":1783361680539,"version":"3.54.6"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:00:00Z","timestamp":1705104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002352","name":"Ain Shams University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002352","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Humans are distinguished by their walking patterns; many approaches, including using various types of sensors, have been used to establish walking patterns as biometrics. By studying the distinguishing features of a person's footsteps, footstep recognition may be utilized in numerous security applications, such as managing access in protected locations or giving an additional layer of biometric verification for secure admittance into restricted regions. We proposed biometric systems for verifying and identifying a person by acquiring spatial foot pressure images from the values obtained from the piezoelectric sensors using the Swansea Foot Biometric Database, which contains 19,980 footstep signals from 127 users and is the most prominent open-source gait database available for footstep recognition. The images acquired are fed into the ConvNeXt model, which was trained using the transfer learning technique, using 16 stride footstep signals in each batch with an Adam optimizer and a learning rate of 0.0001, and using sparse categorical cross-entropy as the loss function. The proposed ConvNeXt model has been adjusted to acquire 512 feature vectors per image, and these feature vectors are used to train the logistic regression models. We propose two biometric systems. The first biometric system is based on training one logistic regression model as a classifier to identify 40 different users using 1600 signals for training, 6697 signals for validation, and 200 signals for evaluation. The second biometric system is based on training 40 logistic regression models, one for each user, to validate the user's authenticity, with a total number of 2363 training signals, 7077 validation signals, and 500 evaluation signals. Each of the 40 models has a 40-training signal per client and a different number of signals per imposter, a different number of signals for the validation that ranges between 8 and 650 signals, a 5-signal for an authenticated client, and a different number of signals per imposter for evaluation. Independent validation and evaluation sets are used to evaluate our systems. In the biometric identification system, we obtained an equal error rate of 15.30% and 21.72% for the validation and evaluation sets, while in the biometric verification system, we obtained an equal error rate of 6.97% and 10.25% for the validation and evaluation sets.<\/jats:p>","DOI":"10.1007\/s00521-023-09390-3","type":"journal-article","created":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T03:02:24Z","timestamp":1705114944000},"page":"3817-3836","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Biometric systems for identification and verification scenarios using spatial footsteps components"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-9238-5875","authenticated-orcid":false,"given":"Ayman","family":"Iskandar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Alfonse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Roushdy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"El-Sayed M.","family":"El-Horbaty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,13]]},"reference":[{"issue":"8","key":"9390_CR1","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1016\/j.patcog.2014.01.016","volume":"47","author":"JA Unar","year":"2014","unstructured":"Unar JA, Seng WC, Abbasi A (2014) A review of biometric technology along with trends and prospects. Pattern Recognit 47(8):2673\u20132688. https:\/\/doi.org\/10.1016\/j.patcog.2014.01.016","journal-title":"Pattern Recognit"},{"key":"9390_CR2","doi-asserted-by":"publisher","unstructured":"Tistarelli M, Li SZ, Chellappa R (2012) Handbook of remote biometrics: for surveillance and security, Springer London. https:\/\/doi.org\/10.1007\/978-1-84882-385-3","DOI":"10.1007\/978-1-84882-385-3"},{"key":"9390_CR3","doi-asserted-by":"publisher","unstructured":"Costilla-Reyes O, Vera-Rodriguez R, Alharthi AS, Yunas SU, Ozanyan KB (2020) Deep learning in gait analysis for security and healthcare. In: Pedrycz W, Chen SM (eds) Deep learning: algorithms and applications. Studies in computational intelligence, vol 865. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-31760-7_10","DOI":"10.1007\/978-3-030-31760-7_10"},{"key":"9390_CR4","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11831-019-09375-3","volume":"28","author":"JP Singh","year":"2021","unstructured":"Singh JP, Jain S, Arora S, Singh UP (2021) A survey of behavioral biometric gait recognition: current success and future perspectives. Arch Comput Methods Eng 28:107\u2013148. https:\/\/doi.org\/10.1007\/s11831-019-09375-3","journal-title":"Arch Comput Methods Eng"},{"key":"9390_CR5","doi-asserted-by":"publisher","unstructured":"Vera-Rodriguez R, Evans NWD, Mason JSD (2009) Footstep recognition. In: Li SZ, Jain A (eds) Encyclopedia of biometrics. Springer, Boston, MA. https:\/\/doi.org\/10.1007\/978-0-387-73003-5_41","DOI":"10.1007\/978-0-387-73003-5_41"},{"key":"9390_CR6","doi-asserted-by":"publisher","unstructured":"Vera-Rodriguez R, Mason JSD, Fierrez J, Ortega-Garcia J (2010) Analysis of time domain information for footstep recognition. In: Bebis G, et al. Advances in visual computing. ISVC 2010. Lecture notes in computer science, vol 6453. Springer, Berlin, Heidelberg. https:\/\/doi.org\/10.1007\/978-3-642-17289-2_47","DOI":"10.1007\/978-3-642-17289-2_47"},{"issue":"6","key":"9390_CR7","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1049\/iet-cvi.2010.0189","volume":"5","author":"R Vera-Rodriguez","year":"2011","unstructured":"Vera-Rodriguez R, Mason JSD, Fierrez J, Ortega-Garcia J (2011) Analysis of spatial domain information for footstep recognition. IET Comput Vis 5(6):380\u2013388. https:\/\/doi.org\/10.1049\/iet-cvi.2010.0189","journal-title":"IET Comput Vis"},{"issue":"4","key":"9390_CR8","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/TPAMI.2012.164","volume":"35","author":"R Vera-Rodriguez","year":"2013","unstructured":"Vera-Rodriguez R, Mason JSD, Fierrez J, Ortega-Garcia J (2013) Comparative analysis and fusion of spatiotemporal information for footstep recognition. IEEE Trans Pattern Anal Mach Intell 35(4):823\u2013834. https:\/\/doi.org\/10.1109\/TPAMI.2012.164","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9390_CR9","doi-asserted-by":"publisher","unstructured":"Vera-Rodriguez R, Lewis RP, Mason JSD, Evans NWD (2008) A large scale footstep database for biometric studies created using cross-biometrics for labelling, In: 2008 10th international conference on control, automation, robotics and vision, Hanoi, Vietnam, p 1361\u20131366, https:\/\/doi.org\/10.1109\/ICARCV.2008.4795721","DOI":"10.1109\/ICARCV.2008.4795721"},{"key":"9390_CR10","doi-asserted-by":"publisher","unstructured":"Costilla-Reyes O, Vera-Rodriguez R, Scully P, Ozanyan KB (2016) Spatial footstep recognition by convolutional neural networks for biometric applications, IEEE SENSORS, Orlando, FL, USA, p 1\u20133, https:\/\/doi.org\/10.1109\/ICSENS.2016.7808890","DOI":"10.1109\/ICSENS.2016.7808890"},{"issue":"2","key":"9390_CR11","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/TPAMI.2018.2799847","volume":"41","author":"O Costilla-Reyes","year":"2019","unstructured":"Costilla-Reyes O, Vera-Rodriguez R, Scully P, Ozanyan KB (2019) Analysis of spatio-temporal representations for robust footstep recognition with deep residual neural networks. IEEE Trans Pattern Anal Mach Intell 41(2):285\u2013296. https:\/\/doi.org\/10.1109\/TPAMI.2018.2799847","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9390_CR12","doi-asserted-by":"publisher","unstructured":"George G, Oommen RM, Shelly S, Philipose SS, Varghese AM (2018) A survey on various median filtering techniques for removal of impulse noise from digital image, In: 2018 conference on emerging devices and smart systems (ICEDSS), Tiruchengode, India, p 235\u2013238, https:\/\/doi.org\/10.1109\/ICEDSS.2018.8544273","DOI":"10.1109\/ICEDSS.2018.8544273"},{"key":"9390_CR13","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/info11060322","volume":"11","author":"J Gibson","year":"2020","unstructured":"Gibson J, Hoontaek O (2020) Mutual information loss in pyramidal image processing. Information 11:6\u2013322. https:\/\/doi.org\/10.3390\/info11060322","journal-title":"Information"},{"key":"9390_CR14","doi-asserted-by":"publisher","unstructured":"Getreuer P (2011) Linear methods for image interpolation, image processing on line, p 238\u2013259, https:\/\/doi.org\/10.5201\/ipol.2011.g_lmii","DOI":"10.5201\/ipol.2011.g_lmii"},{"key":"9390_CR15","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","volume":"9","author":"A Hosna","year":"2022","unstructured":"Hosna A, Merry E, Gyalmo J, Alom Z, Aung Z, Azim MA (2022) Transfer learning: a friendly introduction. J Big Data 9:102. https:\/\/doi.org\/10.1186\/s40537-022-00652-w","journal-title":"J Big Data"},{"key":"9390_CR16","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s, 2022 IEEE\/CVF conference on computer vision and pattern recognition (CVPR), New Orleans, LA, USA, p 11966\u201311976, https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"9390_CR17","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2021) Deep residual learning for image recognition, computer vision and pattern recognition, https:\/\/doi.org\/10.48550\/arXiv.1512.03385","DOI":"10.48550\/arXiv.1512.03385"},{"key":"9390_CR18","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows, computer vision and pattern recognition, https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"key":"9390_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis (IJCV) 115:211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis (IJCV)"},{"key":"9390_CR20","doi-asserted-by":"publisher","unstructured":"Ciampiconi L, Elwood A, Leonardi M, Mohamed A, Rozza A (2023) A survey and taxonomy of loss functions in machine learning. https:\/\/doi.org\/10.48550\/arXiv.2301.05579","DOI":"10.48550\/arXiv.2301.05579"},{"key":"9390_CR21","doi-asserted-by":"publisher","first-page":"006","DOI":"10.1590\/1678-987320287406en","volume":"28","author":"AAT Fernandes","year":"2021","unstructured":"Fernandes AAT, Figueiredo Filho DB, Rocha ECD, Nascimento WDS (2021) Read this paper if you want to learn logistic regression. Rev de soc e polit 28:006. https:\/\/doi.org\/10.1590\/1678-987320287406en","journal-title":"Rev de soc e polit"},{"key":"9390_CR22","doi-asserted-by":"publisher","unstructured":"James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning: with applications in R 2nd edition, Springer New York, NY, p 197\u2013209, https:\/\/doi.org\/10.1007\/978-1-4614-7138-7","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"9390_CR23","doi-asserted-by":"publisher","first-page":"105899","DOI":"10.1016\/j.engappai.2023.105899","volume":"120","author":"Y Akkem","year":"2023","unstructured":"Akkem Y, Biswas SK, Varanasi A (2023) Smart farming using artificial intelligence: a review. Eng Appl Artif Intell 120:105899. https:\/\/doi.org\/10.1016\/j.engappai.2023.105899","journal-title":"Eng Appl Artif Intell"},{"key":"9390_CR24","doi-asserted-by":"publisher","unstructured":"Akkem Y, Biswas SK, Varanasi A (2023) Smart farming monitoring using ML and MLOps. In: Hassanien AE, Castillo O, Anand S, Jaiswal A (eds) International conference on innovative computing and communications (ICICC 2023). Lecture notes in networks and systems, vol 703. Springer, Singapore. https:\/\/doi.org\/10.1007\/978-981-99-3315-0_51","DOI":"10.1007\/978-981-99-3315-0_51"},{"issue":"1","key":"9390_CR25","doi-asserted-by":"publisher","first-page":"3","DOI":"10.54623\/fue.fcij.5.1.3","volume":"5","author":"N Al-Banhawy","year":"2020","unstructured":"Al-Banhawy N, Mohsen H, Ghali N (2020) Signature identification and verification systems: a comparative study on the online and offline techniques. Future Comput Inform J 5(1):3. https:\/\/doi.org\/10.54623\/fue.fcij.5.1.3","journal-title":"Future Comput Inform J"},{"key":"9390_CR26","doi-asserted-by":"publisher","unstructured":"Sivaram M, Ahamed AM, Yuvaraj D, Megala G, Porkodi V, Kandasamy M (2019) Biometric security and performance metrics: FAR, FER, CER, FRR, In: 2019 international conference on computational intelligence and knowledge economy (ICCIKE), Dubai, United Arab Emirates, p 770\u2013772, https:\/\/doi.org\/10.1109\/ICCIKE47802.2019.9004275","DOI":"10.1109\/ICCIKE47802.2019.9004275"},{"key":"9390_CR27","doi-asserted-by":"publisher","DOI":"10.5772\/52084","author":"M El-Abed","year":"2012","unstructured":"El-Abed M, Charrier C, Rosenberger C (2012) Evaluation of biometric systems new trends and developments in biometrics. INTECH. https:\/\/doi.org\/10.5772\/52084","journal-title":"INTECH"},{"key":"9390_CR28","doi-asserted-by":"crossref","unstructured":"Martin A, Doddington G, Kamm T, Ordowski M, Przybocki M (1997) The DET curve in assessment of detection task performance. In: Proceeding of the European conference on speech communication and technology (EUROSPEECH), Rhodes, Greece, https:\/\/api.semanticscholar.org\/CorpusID:9497630","DOI":"10.21437\/Eurospeech.1997-504"},{"key":"9390_CR29","doi-asserted-by":"publisher","unstructured":"Navratil J, Klusacek D (2007) On Linear DETs, In: 2007 IEEE international conference on acoustics, speech and signal processing\u2014ICASSP '07, Honolulu, HI, USA, p 229\u2013232, https:\/\/doi.org\/10.1109\/ICASSP.2007.367205","DOI":"10.1109\/ICASSP.2007.367205"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09390-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09390-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09390-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T14:10:08Z","timestamp":1707747008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09390-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,13]]},"references-count":29,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["9390"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09390-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,13]]},"assertion":[{"value":"17 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no competing interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The submitted work is original, and the manuscript has not been submitted to another journal for simultaneous consideration.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and Consent to participate"}},{"value":"The authors declare that they consent to publish the article.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}