{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T21:27:51Z","timestamp":1784928471735,"version":"3.55.0"},"reference-count":65,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Gheorghe Asachi Technical University of Iasi, Romania"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.<\/jats:p>","DOI":"10.3390\/s22145160","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:06:21Z","timestamp":1657497981000},"page":"5160","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":119,"title":["Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4752-7884","authenticated-orcid":false,"given":"Sandeep","family":"Kumar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Vijaywada 522302, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5809-6269","authenticated-orcid":false,"given":"Shilpa","family":"Rani","sequence":"additional","affiliation":[{"name":"Department of IT, Neil Gogte Institute of Technology, Kachawanisingaram Village, Hyderabad 500039, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arpit","family":"Jain","sequence":"additional","affiliation":[{"name":"College of Computing Sciences and IT, Teerthanker Mahaveer University, Moradabad 244001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9925-112X","authenticated-orcid":false,"given":"Chaman","family":"Verma","sequence":"additional","affiliation":[{"name":"Department of Media and Educational Informatics, Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University, 1053 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7277-4377","authenticated-orcid":false,"given":"Maria Simona","family":"Raboaca","sequence":"additional","affiliation":[{"name":"National Research and Development Institute for Cryogenic and Isotopic Technologies\u2014ICSI Rm, 240050 Ramnicu Valcea, Romania"},{"name":"Doctoral School, Polytechnic University of Bucharest, 313 Splaiul Independentei, 060042 Bucharest, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zolt\u00e1n","family":"Ill\u00e9s","sequence":"additional","affiliation":[{"name":"Department of Media and Educational Informatics, Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University, 1053 Budapest, Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8413-0317","authenticated-orcid":false,"given":"Bogdan Constantin","family":"Neagu","sequence":"additional","affiliation":[{"name":"Department of Power Engineering, \u201cGheorghe Asachi\u201d Technical University of Iasi, 700050 Iasi, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,9]]},"reference":[{"key":"ref_1","unstructured":"Bruno, P., Michelassi, C., and Rocha, A. (2011, January 11\u201314). Face liveness detection under bad illumination conditions. Proceedings of the 18th IEEE International Conference on Image Processing, Brussels, Belgium."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, J., Lei, Z., Liao, S., and Li, S.Z. (2013, January 4\u20137). Face liveness detection with component dependent descriptor. Proceedings of the IEEE International Conference on Biometrics (ICB), Madrid, Spain.","DOI":"10.1109\/ICB.2013.6612955"},{"key":"ref_3","unstructured":"Jukka, K., Hadid, A., and Pietik\u00e4inen, M. (October, January 29). Context-based face anti-spoofing. Proceedings of the Sixth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), Arlington, VA, USA."},{"key":"ref_4","unstructured":"Yaman, A., \u015eeng\u00fcr, A., Budak, \u00dc., and Ekici, S. (2017, January 16\u201317). Deep learning-based face liveness detection in videos. Proceedings of the IEEEInternational Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey."},{"key":"ref_5","unstructured":"Quoc-Tin, P., Dang-Nguyen, D., Boato, G., and de Natale, F.G.B. (2016, January 25\u201328). Face spoofing detection using LDP-TOP. Proceedings of the IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Simanjuntak, G.D., Ramadhani, K.N., and Arifianto, A. (2019, January 24\u201326). Face Spoofing Detection using Color Distortion Features and Principal Component Analysis. Proceedings of the 7th IEEE International Conference on Information and Communication Technology (ICoICT), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICoICT.2019.8835343"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1109\/TPAMI.2007.70800","article-title":"Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces","volume":"30","author":"Makinen","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Li, S., Liu, P., and Dai, Q. (2014, January 20\u201321). Multi-feature deep learning for face gender recognition. Proceedings of the 7thIEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China.","DOI":"10.1109\/ITAIC.2014.7065102"},{"key":"ref_9","unstructured":"Chen, H., and Wei, W. (2006, January 21\u201323). Pseudo-Example Based Iterative SVM Learning Approach for Gender Classification. Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kabasakal, B., and S\u00fcmer, E. (2018, January 2\u20135). Gender recognition using innovative pattern recognition techniques. Proceedings of the 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404306"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Mayo, M., and Zhang, E. (2008, January 26\u201328). Improving face gender classification by adding deliberately misaligned faces to the training data. Proceedings of the 23rd International Conference Image and Vision Computing New Zealand, Christchurch, New Zealand.","DOI":"10.1109\/IVCNZ.2008.4762066"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shabanian, M., Eckstein, E.C., Chen, H., and DeVincenzo, J.P. (2019, January 18\u201321). Classification of Neurodevelopmental Age in Normal Infants Using 3D-CNN based on Brain MRI. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), San Diego, CA, USA.","DOI":"10.1109\/BIBM47256.2019.8983399"},{"key":"ref_13","unstructured":"Aydogdu, M.F., Celik, V., and Demirci, M.F. (February, January 30). Comparison of Three Different CNN Architectures for Age Classification. Proceedings of the 11th IEEE International Conference on Semantic Computing (ICSC), San Diego, CA, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zheng, T., Deng, W., and Hu, J. (2017, January 10\u201313). Deep Probabilities for Age Estimation. Proceedings of the IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305123"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, S., Zhang, C., Dong, M., Le, J., and Rao, M. (2017, January 21\u201326). Using Ranking-CNN for Age Estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.86"},{"key":"ref_16","first-page":"1","article-title":"Object-Based Image Retrieval Using the U-Net-Based Neural Network","volume":"21","author":"Kumar","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.cviu.2008.08.001","article-title":"Pupil dilation degrades iris biometric performance","volume":"113","author":"Hollingsworth","year":"2009","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1049\/iet-cvi.2010.0165","article-title":"Analysis of Physical Ageing Effects in Iris Biometrics","volume":"5","author":"Fairhurst","year":"2011","journal-title":"IET Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bekhouche, S.E., Ouafi, A., Benlamoudi, A., Taleb-Ahmed, A., and Hadid, A. (2015, January 25\u201327). Facial age estimation and gender classification using multi level local phase quantization. Proceedings of the 3rd International Conference on Control, Engineering and Information Technology (CEIT), Tlemcen, Algeria.","DOI":"10.1109\/CEIT.2015.7233141"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, J., Hu, C., and Pan, J. (2017, January 3\u20135). Deep convolutional neural networks-based age and gender classification with facial images. Proceedings of the First International Conference on Electronics Instrumentation and Information Systems (EIIS), Harbin, China.","DOI":"10.1109\/EIIS.2017.8298719"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Dileep, M.R., and Danti, A. (2016, January 24\u201326). Multiple hierarchical decision on neural network to predict human age and gender. Proceedings of the International Conference on Emerging Trends in Engineering, Technology and Science (ICE.TETS), Pudukkottai, India.","DOI":"10.1109\/ICETETS.2016.7603026"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hu, K., Liu, C., Yu, X., Zhang, J., He, Y., and Zhu, H. (2018, January 20\u201322). A 2.5D Cancer Segmentation for MRI Images Based on U-Net. Proceedings of the 5th International Conference on Information Science and Control Engineering (ICISCE), Zhengzhou, China.","DOI":"10.1109\/ICISCE.2018.00011"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hosseini, S., and Cho, N.I. (2019, January 14\u201318). Gf-capsnet: Using Gabor jet and capsule networks for facial age, gender, and expression recognition. Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), Lille, France.","DOI":"10.1109\/FG.2019.8756552"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gurnani, A., Shah, K., Gajjar, V., Mavani, V., and Khandhediya, Y. (2019, January 7\u201311). SAF-BAGE: Salient Approach for Facial Soft-Biometric Classification-Age, Gender, Facial Expression. Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV.2019.00094"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yu, Z., Zhao, C., Wang, Z., Qin, Y., Su, Z., Li, X., Zhou, F., and Zhao, G. (2020, January 13\u201319). Searching Central Difference Convolutional Networks for Face Anti-Spoofing. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00534"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1109\/JBHI.2021.3107735","article-title":"Lightweight Face Anti-Spoofing Network for Telehealth Applications","volume":"26","author":"Lin","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1109\/TCDS.2021.3064679","article-title":"Data Fusion based Two-stage Cascade Framework for Multi-Modality Face Anti-Spoofing","volume":"14","author":"Liu","year":"2022","journal-title":"IEEE Trans. Cogn. Dev. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Levi, G., and Hassner, T. (2015, January 7\u201312). Age and gender classification using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1109\/TIFS.2014.2359646","article-title":"Age and gender estimation of unfiltered faces","volume":"9","author":"Eidinger","year":"2014","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_30","unstructured":"Dehghan, A., Ortiz, E.G., Shu, G., and Masood, S.Z. (2017). Dager: Deep age, gender and emotion recognition using convolutional neural network. arXiv."},{"key":"ref_31","unstructured":"Zakariya, Q., Mallouh, A.A., and Barkana, B.D. (2017). Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model. arXiv."},{"key":"ref_32","unstructured":"Liao, Z., Petridis, S., and Pantic, M. (2017). Local Deep Neural Networks for Age and Gender Classification. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hassner, T., Harel, S., Paz, E., and Enbar, R. (2015, January 7\u201312). Effective face frontalization in unconstrained images. Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299058"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Misra, N.R., Kumar, S., and Jain, A. (2021, January 19\u201320). A Review on E-waste: Fostering the Need for Green Electronics. Proceedings of the IEEE International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS51004.2021.9397191"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"946","DOI":"10.1016\/j.imavis.2012.07.009","article-title":"Learning-based encoding with soft assignment for age estimation under unconstrained imaging conditions","volume":"30","author":"Alnajar","year":"2012","journal-title":"Image Vis. Comput."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fazl-Ersi, E., Mousa-Pasandi, M.E., Laganiere, R., and Awad, M. (2014, January 27\u201330). Age and gender recognition using informative features of various types. Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026190"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"66553","DOI":"10.1109\/ACCESS.2020.2985453","article-title":"A Face Spoofing Detection Method Based on Domain Adaptation and Lossless Size Adaptation","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.patrec.2015.09.014","article-title":"On using periocular biometric for gender classification in the wild","volume":"82","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mery, D., and Bowyer, K. (2014). Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-16181-5_59"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/TIFS.2013.2242063","article-title":"Gender classification based on the fusion of different spatial scale features selected by mutual information from the histogram of LBP, intensity, and shape","volume":"8","author":"Tapia","year":"2013","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.patrec.2015.11.015","article-title":"Local Deep Neural Networks for gender recognition","volume":"70","author":"Mansanet","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3721","DOI":"10.1109\/TIP.2012.2197628","article-title":"Facial Expression Recognition in PerceptualColor Space","volume":"21","author":"Lajevardi","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TAFFC.2014.2386334","article-title":"Automatic facial expression recognition using features of salient facial patches","volume":"6","author":"Happy","year":"2014","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_44","first-page":"131","article-title":"Face Spoofing Detection Using Improved SegNet Architecture with Blur Estimation Technique","volume":"13","author":"Kumar","year":"2020","journal-title":"Int. J. Biom."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kumar, S., Singh, S., and Kumar, J. (2019, January 7\u20139). Gender Classification Using Machine Learning with Multi-Feature Method. Proceedings of the IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC.2019.8666601"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1007\/s11277-018-5913-0","article-title":"Live Detection of Face Using Machine Learning with Multi-feature Method","volume":"103","author":"Kumar","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1007\/s11277-018-5923-y","article-title":"Automatic Live Facial Expression Detection Using Genetic Algorithm with Haar Wavelet Features and SVM","volume":"103","author":"Kumar","year":"2018","journal-title":"Wirel. Pers. Commun."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kumar, S., Singh, S., and Kumar, J. (2019). Multiple Face Detection Using Hybrid Features with SVM Classifier. Data and Communication Networks, Springer.","DOI":"10.1007\/978-981-13-2254-9_23"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Kumar, S., Singh, S., and Kumar, J. (2017, January 5\u20136). A Study on Face Recognition Techniques with Age and Gender Classification. Proceedings of the IEEE International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229960"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kumar, S., Singh, S., and Kumar, J. (2017, January 5\u20136). A Comparative Study on Face Spoofing Attacks. Proceedings of the IEEE International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229961"},{"key":"ref_51","first-page":"197","article-title":"Automatic Face Detection Using Genetic Algorithm for Various Challenges","volume":"2","author":"Kumar","year":"2017","journal-title":"Int. J. Sci. Res. Mod. Educ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2500","DOI":"10.1109\/TIFS.2019.2902823","article-title":"Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes","volume":"14","author":"Xie","year":"2019","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"333","DOI":"10.26599\/TST.2018.9010090","article-title":"Exploiting effective facial patches for robust gender recognition","volume":"24","author":"Cheng","year":"2019","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"758","DOI":"10.1109\/TIFS.2017.2766583","article-title":"An Ensemble CNN2ELM for Age Estimation","volume":"13","author":"Duan","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"22492","DOI":"10.1109\/ACCESS.2017.2761849","article-title":"Age Group and Gender Estimation in the Wild with Deep RoR Architecture","volume":"5","author":"Zhang","year":"2017","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"170116","DOI":"10.1109\/ACCESS.2019.2955383","article-title":"A Cascade Face Spoofing Detector Based on Face Anti-Spoofing R-CNN and Improved Retinex LBP","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Peng, Z., Li, X., and Yan, F. (2020, January 11\u201312). An Adaptive Deep Learning Model for Smart Home Autonomous System. Proceedings of the IEEE International Conference on Intelligent Transportation, Big Data and Smart City (ICITBS), Vientiane, Laos.","DOI":"10.1109\/ICITBS49701.2020.00156"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Tai, C.-S., Hong, J.-H., and Fu, L.-C. (2019, January 6\u20139). A Real-time Demand-side Management System Considering User Behavior Using Deep Q-Learning in Home Area Network. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy.","DOI":"10.1109\/SMC.2019.8914266"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Lee, S.-H., and Yang, C.-S. (2017, January 12\u201314). An intelligent home access control system using deep neural network. Proceedings of the IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Taipei, Taiwan.","DOI":"10.1109\/ICCE-China.2017.7991105"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/j.jksuci.2018.09.019","article-title":"Classification and reconstruction algorithms for the archaeological fragments","volume":"32","author":"Rasheed","year":"2020","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Hung, Y.P., Huang, T.M., Li, K.T., Rajapakse, R.J., Chen, Y.S., Han, P.H., Liu, I.S., Wang, H.C., and Yi, D.C. (2018, January 26\u201330). Simulating the Activity of Archaeological Excavation in the Immersive Virtual Reality. Proceedings of the 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 24th IEEE International Conference on Virtual Systems and Multimedia (VSMM 2018), San Francisco, CA, USA.","DOI":"10.1109\/DigitalHeritage.2018.8810075"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Galli, M., Inglese, C., Ismaelli, T., and Griffo, M. (2018, January 26\u201330). Rome under Rome: Survey and analysis of the east excavation area beneath the Basilica Iulia. Proceedings of the 3rd Digital Heritage International Congress (DigitalHERITAGE) held jointly with 24th IEEE International Conference on Virtual Systems and Multimedia (VSMM 2018)), San Francisco, CA, USA.","DOI":"10.1109\/DigitalHeritage.2018.8810042"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Agapiou, A., and Sarris, A. (2018). Beyond GIS Layering: Challenging the (Re)use and Fusion of Archaeological Prospection Data Based on Bayesian Neural Networks (BNN). Remote Sens., 10.","DOI":"10.3390\/rs10111762"},{"key":"ref_64","unstructured":"Lyons, M.J., Kamachi, M., and Gyoba, J. (2020). Coding Facial Expressions with Gabor Wavelets (IVC Special Issue). arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Lyons, M.J. (2021). \u201cExcavating AI\u201d Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset. arXiv.","DOI":"10.31234\/osf.io\/bvf2s"}],"updated-by":[{"DOI":"10.3390\/s23218825","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,7,9]],"date-time":"2022-07-09T00:00:00Z","timestamp":1657324800000}}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5160\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T13:47:41Z","timestamp":1754228861000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5160"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,9]]},"references-count":65,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145160"],"URL":"https:\/\/doi.org\/10.3390\/s22145160","relation":{"correction":[{"id-type":"doi","id":"10.3390\/s23218825","asserted-by":"object"}]},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,9]]}}}