{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:03:33Z","timestamp":1772042613598,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T00:00:00Z","timestamp":1702944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU)","award":["IMSIU-RG23082"],"award-info":[{"award-number":["IMSIU-RG23082"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Biometric authentication is a widely used method for verifying individuals\u2019 identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model\u2019s performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.<\/jats:p>","DOI":"10.3390\/s24010015","type":"journal-article","created":{"date-parts":[[2023,12,19]],"date-time":"2023-12-19T11:17:24Z","timestamp":1702984644000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Novel PPG-Based Biometric Authentication System Using a Hybrid CVT-ConvMixer Architecture with Dense and Self-Attention Layers"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0730-6857","authenticated-orcid":false,"given":"Mostafa E. A.","family":"Ibrahim","sequence":"first","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"},{"name":"Department of Electrical Engineering, Benha Faculty of Engineering, Benha University, Benha 13518, Qalubia, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-1363","authenticated-orcid":false,"given":"Qaisar","family":"Abbas","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}]},{"given":"Yassine","family":"Daadaa","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6692-4703","authenticated-orcid":false,"given":"Alaa E. S.","family":"Ahmed","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"},{"name":"Electrical Engineering Department, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3603705","article-title":"Challenges and Opportunities of Biometric User Authentication in the Age of IoT: A Survey","volume":"56","author":"Lien","year":"2023","journal-title":"ACM Comput. Surv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"11625","DOI":"10.1007\/s00521-023-08539-4","article-title":"Influencing brain waves by evoked potentials as biometric approach: Taking stock of the last six years of research","volume":"35","author":"Saia","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hnoohom, N., Mekruksavanich, S., and Jitpattanakul, A. (2023). Physical Activity Recognition Based on Deep Learning Using Photoplethysmography and Wearable Inertial Sensors. Electronics, 12.","DOI":"10.3390\/electronics12030693"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"906324","DOI":"10.3389\/felec.2022.906324","article-title":"Reliability of pulse photoplethysmography sensors: Coverage using different setups and body locations","volume":"3","author":"Kontaxis","year":"2022","journal-title":"Front. Electron."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sinnapolu, G., Alawneh, S., and Dixon, S.R. (2023). Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform. Mathematics, 11.","DOI":"10.3390\/math11081781"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1016\/j.inffus.2022.09.031","article-title":"Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends","volume":"90","author":"Qureshi","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Baig, A.R., and Hussain, A. (2023). Classification of Post-COVID-19 Emotions with Residual-Based Separable Convolution Networks and EEG Signals. Sustainability, 15.","DOI":"10.3390\/su15021293"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patrec.2022.03.006","article-title":"Photoplethysmographic biometrics: A comprehensive survey","volume":"156","author":"Labati","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1109\/JSEN.2023.3240854","article-title":"On-Device Deep Learning for Mobile and Wearable Sensing Applications: A Review","volume":"23","author":"Incel","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_10","first-page":"2203","article-title":"Deep Learning for PPG-Based Biometric Identification","volume":"13","author":"Li","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/TBCAS.2019.2892297","article-title":"CorNET: Deep Learning Framework for PPG-Based Heart Rate Estimation and Biometric Identification in Ambulant Environment","volume":"13","author":"Biswas","year":"2019","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Hazratifard, M., Agrawal, V., Gebali, F., Elmiligi, H., and Mamun, M. (2023). Ensemble Siamese Network (ESN) Using ECG Signals for Human Authentication in Smart Healthcare System. Sensors, 23.","DOI":"10.3390\/s23104727"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Reiss, A., Indlekofer, I., Schmidt, P., and Van Laerhoven, K. (2019). Deep PPG: Large-Scale Heart Rate Estimation with Convolutional Neural Networks. Sensors, 19.","DOI":"10.3390\/s19143079"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hwang, D.Y., Taha, B., and Hatzinakos, D. (2021, January 6\u201311). Variation-Stable Fusion for PPG-Based Biometric System. Proceedings of the ICASSP 2021\u20142021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada. Available online: https:\/\/ieeexplore.ieee.org\/document\/9413906\/.","DOI":"10.1109\/ICASSP39728.2021.9413906"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"787","DOI":"10.1007\/s11265-022-01747-6","article-title":"A New Score Level Fusion Approach for Stable User Verification System Using the PPG Signal","volume":"94","author":"Hwang","year":"2022","journal-title":"J. Signal Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, L., Li, A., Chen, S., Ren, W., and Choo, K.-K.R. (2023). A Secure, Flexible and PPG-based Biometric Scheme for Healthy IoT Using Homomorphic Random Forest. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2023.3285796"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Seok, C.L., Song, Y.D., An, B.S., and Lee, E.C. (2023). Photoplethysmogram Biometric Authentication Using a 1D Siamese Network. Sensors, 23.","DOI":"10.3390\/s23104634"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s11633-022-1366-8","article-title":"Dual-domain and Multiscale Fusion Deep Neural Network for PPG Biometric Recognition","volume":"20","author":"Liu","year":"2023","journal-title":"Mach. Intell. Res."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ortiz, B.L., Miller, E., Dallas, T., and Chong, J.W. (November, January 30). Time-Series Forecasting: Extreme Gradient Boosting Implementation in Smartphone Photoplethysmography Signals for Biometric Authentication Processes. Proceedings of the 2022 IEEE Sensors, Dallas, TX, USA. Available online: https:\/\/ieeexplore.ieee.org\/document\/9967189\/.","DOI":"10.1109\/SENSORS52175.2022.9967189"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Luque, J., Cortes, G., Segura, C., Maravilla, A., Esteban, J., and Fabregat, J. (2018, January 3\u20137). End-to-end photopleth ysmography (PPG) based biometric authentication by using convolutional neural networks. Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy. Available online: https:\/\/ieeexplore.ieee.org\/document\/8553585.","DOI":"10.23919\/EUSIPCO.2018.8553585"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1109\/TIFS.2020.3006313","article-title":"Evaluation of the Time Stability and Uniqueness in PPG-Based Biometric System","volume":"16","author":"Hwang","year":"2020","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhao, T., Wang, Y., Liu, J., Chen, Y., Cheng, J., and Yu, J. (2020, January 6\u20139). TrueHeart: Continuous authentication on wrist-worn wearables using PPG-based biometrics. Proceedings of the IEEE INFOCOM 2020\u2014IEEE Conference on Computer Communications, Toronto, ON, Canada. Available online: https:\/\/ieeexplore.ieee.org\/document\/9155526.","DOI":"10.1109\/INFOCOM41043.2020.9155526"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., and Zhang, H. (2023, January 19\u201321). A PPG-based biometric system using fuzzy min-max model. Proceedings of the Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), Hulun Buir, China.","DOI":"10.1117\/12.2660113"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Coelho, K.K., Trist\u00e3o, E.T., Nogueira, M., Vieira, A.B., and Nacif, J.A. (2023). Multimodal biometric authentication method by federated learning. Biomed. Signal Process. Control., 85.","DOI":"10.1016\/j.bspc.2023.105022"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Aly, H., and Di Pietro, R. (TechRxiv, 2023). Towards Feasible Continuous Authentication Using PPG Signal with Deep Autoencoders, TechRxiv, preprint.","DOI":"10.36227\/techrxiv.22769783.v1"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ortiz, B.L., Gupta, V., Chong, J.W., Jung, K., and Dallas, T. (2022). User Authentication Recognition Process Using Long Short-Term Memory Model. Multimodal Technol. Interact., 6.","DOI":"10.3390\/mti6120107"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4675","DOI":"10.1109\/JSEN.2022.3146291","article-title":"Novel Robust Photoplethysmogram-Based Authentication","volume":"22","author":"Pu","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ahamed, F., Farid, F., Suleiman, B., Jan, Z., Wahsheh, L.A., and Shahrestani, S. (2022). An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services. Futur. Internet, 14.","DOI":"10.20944\/preprints202206.0223.v1"},{"key":"ref_29","unstructured":"Donida Labati, R., Piuri, V., Rundo, F., Scotti, F., and Spampinato, C. (2021). International Conference on Pattern Recognition, Springer International Publishing."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e4685","DOI":"10.1002\/dac.4685","article-title":"Biosignal classification for human identification based on convolutional neural networks","volume":"34","author":"Siam","year":"2021","journal-title":"Int. J. Commun. Syst."},{"key":"ref_31","unstructured":"Siam, A.I., EI-Samie, F.A., Elazm, A.A., EI-Bahnawawy, N., and Elbanby, G. (2022, November 10). Real-World PPG Dataset Mendeley Data. Available online: https:\/\/data.mendeley.com\/datasets\/yynb8t9x3d\/1."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_33","unstructured":"(2022, January 23). Physionet Dataset. Available online: https:\/\/archive.physionet.org\/cgi-bin\/ATM?database=%20mimic2db."},{"key":"ref_34","first-page":"69","article-title":"Multiresolution approximations and wavelet orthonormal bases of L2 (R)","volume":"315","author":"Mallat","year":"1989","journal-title":"Trans. Am. Math. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Hussain, A., and Baig, A.R. (2022). Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers. Diagnostics, 12.","DOI":"10.3390\/diagnostics12123109"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Abbas, Q., Albalawi, T.S., Perumal, G., and Celebi, M.E. (2023). Automatic Face Recognition System Using Deep Convolutional Mixer Architecture and AdaBoost Classifier. Appl. Sci., 13.","DOI":"10.3390\/app13179880"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/15\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:41:37Z","timestamp":1760132497000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,19]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010015"],"URL":"https:\/\/doi.org\/10.3390\/s24010015","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,19]]}}}