{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T02:08:43Z","timestamp":1781057323299,"version":"3.54.1"},"reference-count":46,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,10]],"date-time":"2018-11-10T00:00:00Z","timestamp":1541808000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In-air signature is a new modality which is essential for user authentication and access control in noncontact mode and has been actively studied in recent years. However, it has been treated as a conventional online signature, which is essentially a 2D spatial representation. Notably, this modality bears a lot more potential due to an important hidden depth feature. Existing methods for in-air signature verification neither capture this unique depth feature explicitly nor fully explore its potential in verification. Moreover, these methods are based on heuristic approaches for fingertip or hand palm center detection, which are not feasible in practice. Inspired by the great progress in deep-learning-based hand pose estimation, we propose a real-time in-air signature acquisition method which estimates hand joint positions in 3D using a single depth image. The predicted 3D position of fingertip is recorded for each frame. We present four different implementations of a verification module, which are based on the extracted depth and spatial features. An ablation study was performed to explore the impact of the depth feature in particular. For matching, we employed the most commonly used multidimensional dynamic time warping (MD-DTW) algorithm. We created a new database which contains 600 signatures recorded from 15 different subjects. Extensive evaluations were performed on our database. Our method, called 3DAirSig, achieved an equal error rate (EER) of     0.46    %. Experiments showed that depth itself is an important feature, which is sufficient for in-air signature verification.<\/jats:p>","DOI":"10.3390\/s18113872","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T10:58:22Z","timestamp":1542193102000},"page":"3872","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["3DAirSig: A Framework for Enabling In-Air Signatures Using a Multi-Modal Depth Sensor"],"prefix":"10.3390","volume":"18","author":[{"given":"Jameel","family":"Malik","sequence":"first","affiliation":[{"name":"German Research Center for Artificial Intelligence, DFKI, Kaiserslautern 67653, Germany"},{"name":"Department of Informatics, University of Kaiserslautern, Kaiserslautern 67653, Germany"},{"name":"School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed","family":"Elhayek","sequence":"additional","affiliation":[{"name":"University of Prince Mugrin (UPM), Madinah 20012, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4239-6520","authenticated-orcid":false,"given":"Sheraz","family":"Ahmed","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence, DFKI, Kaiserslautern 67653, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Faisal","family":"Shafait","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"},{"name":"Deep Learning Laboratory, National Center of Artificial Intelligence (NCAI), Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Imran","family":"Malik","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"},{"name":"Deep Learning Laboratory, National Center of Artificial Intelligence (NCAI), Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Didier","family":"Stricker","sequence":"additional","affiliation":[{"name":"German Research Center for Artificial Intelligence, DFKI, Kaiserslautern 67653, Germany"},{"name":"Department of Informatics, University of Kaiserslautern, Kaiserslautern 67653, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sajid, H., and Sen-ching, S.C. (2015, January 16\u201319). VSig: Hand-gestured signature recognition and authentication with wearable camera. Proceedings of the 2015 IEEE International Workshop on Information Forensics and Security (WIFS), Rome, Italy.","DOI":"10.1109\/WIFS.2015.7368566"},{"key":"ref_2","unstructured":"Bush, L., Carr, S., Hall, J., Saulson, J., and Scott-Simmons, W. (2016, January 7). Creating a \u201cClassroom of the Future\u201d for P-12 Pre-Service Educators. Proceedings of the Society for Information Technology & Teacher Education International Conference, Savannah, GA, USA."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Robert, S.N., and Thilagavathi, B. (2015, January 19\u201320). Offline signature verification using support vectore machine. Proceedings of the 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India.","DOI":"10.1109\/ICIIECS.2015.7193005"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chandra, S., and Maheskar, S. (2016, January 3\u20135). Offline signature verification based on geometric feature extraction using artificial neural network. Proceedings of the 2016 3rd International Conference on Recent Advances in Information Technology (RAIT), Dhanbad, India.","DOI":"10.1109\/RAIT.2016.7507937"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1016\/j.protcy.2013.12.445","article-title":"Offline signature verification using pixel matching technique","volume":"10","author":"Bhattacharya","year":"2013","journal-title":"Procedia Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2483","DOI":"10.1016\/j.patrec.2005.05.005","article-title":"A comparative study on the consistency of features in on-line signature verification","volume":"26","author":"Lei","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.patcog.2013.06.026","article-title":"Legendre polynomials based feature extraction for online signature verification. Consistency analysis of feature combinations","volume":"47","author":"Parodi","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fayyaz, M., Saffar, M.H., Sabokrou, M., Hoseini, M., and Fathy, M. (2015, January 3\u20135). Online signature verification based on feature representation. Proceedings of the 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP), Mashhad, Iran.","DOI":"10.1109\/AISP.2015.7123528"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1109\/TSMCB.2007.895323","article-title":"On using the Viterbi path along with HMM likelihood information for online signature verification","volume":"37","author":"Van","year":"2007","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Alonso-Fernandez, F., Fierrez-Aguilar, J., and Ortega-Garcia, J. (2005). Sensor interoperability and fusion in signature verification: A case study using tablet pc. Advances in Biometric Person Authentication, Springer.","DOI":"10.1007\/11569947_23"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/34.506415","article-title":"Reliable on-line human signature verification systems","volume":"18","author":"Lee","year":"1996","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Iranmanesh, V., Ahmad, S.M.S., Adnan, W.A.W., Malallah, F.L., and Yussof, S. (2013, January 2\u20134). Online signature verification using neural network and pearson correlation features. Proceedings of the 2013 IEEE Conference on Open Systems (ICOS), Kuching, Malaysia.","DOI":"10.1109\/ICOS.2013.6735040"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1109\/TSMCB.2009.2034382","article-title":"Online signature verification with support vector machines based on LCSS kernel functions","volume":"40","author":"Gruber","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Martens, R., and Claesen, L. (1996, January 25\u201329). On-line signature verification by dynamic time-warping. Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria.","DOI":"10.1109\/ICPR.1996.546791"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2943","DOI":"10.1016\/S0167-8655(03)00155-7","article-title":"Online signature verification using a new extreme points warping technique","volume":"24","author":"Feng","year":"2003","journal-title":"Pattern Recognit. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compeleceng.2016.11.010","article-title":"A novel video-based system for in-air signature verification","volume":"57","author":"Fang","year":"2017","journal-title":"Comput. Electr. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jeon, J.H., Oh, B.S., and Toh, K.A. (2012, January 5\u20137). A system for hand gesture based signature recognition. Proceedings of the 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV), Guangzhou, China.","DOI":"10.1109\/ICARCV.2012.6485153"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2468","DOI":"10.1016\/j.patcog.2011.04.010","article-title":"Analysis of pattern recognition techniques for in-air signature biometrics","volume":"44","author":"Bailador","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Khoh, W.H., Pang, Y.H., and Teoh, A.B.J. (2018). In-air hand gesture signature recognition system based on 3-dimensional imagery. Multimed. Tools Appl., 1\u201325.","DOI":"10.1007\/s11042-018-6458-7"},{"key":"ref_20","unstructured":"Zhou, X., Wan, Q., Zhang, W., Xue, X., and Wei, Y. (arxiv, 2016). Model-based deep hand pose estimation, arxiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Malik, J., Elhayek, A., and Stricker, D. (2017, January 10\u201312). Simultaneous Hand Pose and Skeleton Bone-Lengths Estimation from a Single Depth Image. Proceedings of the 2017 International Conference on 3D Vision (3DV), Qingdao, China.","DOI":"10.1109\/3DV.2017.00069"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yuan, S., Garcia-Hernando, G., Stenger, B., Moon, G., Chang, J.Y., Lee, K.M., Molchanov, P., Kautz, J., Honari, S., and Ge, L. (2018, January 19\u201321). Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, Utah.","DOI":"10.1109\/CVPR.2018.00279"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kumar, A., and Bhatia, K. (October, January 30). A survey on offline handwritten signature verification system using writer dependent and independent approaches. Proceedings of the 2016 2nd International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Fall), Bareilly, India.","DOI":"10.1109\/ICACCAF.2016.7748998"},{"key":"ref_24","first-page":"337","article-title":"A survey on offline signature verification","volume":"2","author":"Yadav","year":"2013","journal-title":"Int. J. Eng. Innov. Technol. Vol."},{"key":"ref_25","unstructured":"Dalal, S., and Jindal, U. (2016, January 16\u201318). Performance of integrated signature verification approach. Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India."},{"key":"ref_26","unstructured":"Katagiri, M., and Sugimura, T. (2002, January 3\u201325). Personal authentication by free space signing with video capture. Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Australia."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Takeuchi, A., Manabe, Y., and Sugawara, K. (2013, January 2\u20134). Multimodal soft biometrie verification by hand shape and handwriting motion in the air. Proceedings of the 2013 International Joint Conference on Awareness Science and Technology & Ubi-Media Computing (iCAST 2013 & UMEDIA 2013), Aizu-Wakamatsu, Japan.","DOI":"10.1109\/ICAwST.2013.6765417"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Diep, N.N., Pham, C., and Phuong, T.M. (2015). SigVer3D: Accelerometer Based Verification of 3-D Signatures on Mobile Devices. Knowledge and Systems Engineering, Springer.","DOI":"10.1007\/978-3-319-11680-8_28"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Matsuo, K., Okumura, F., Hashimoto, M., Sakazawa, S., and Hatori, Y. (2007). Arm swing identification method with template update for long term stability. International Conference on Biometrics, Springer.","DOI":"10.1007\/978-3-540-74549-5_23"},{"key":"ref_30","unstructured":"Creative (2018, November 07). Senz3D Interactive Gesture Camera. Available online: https:\/\/asia.creative.com\/p\/web-cameras\/creative-senz3d."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guo, H., Wang, G., Chen, X., Zhang, C., Qiao, F., and Yang, H. (2017, January 17\u201320). Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297136"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1145\/2629500","article-title":"Real-time continuous pose recovery of human hands using convolutional networks","volume":"33","author":"Tompson","year":"2014","journal-title":"ACM Trans. Graph."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tang, D., Jin Chang, H., Tejani, A., and Kim, T.K. (2014, January 24\u201327). Latent regression forest: Structured estimation of 3d articulated hand posture. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.490"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, X., Wei, Y., Liang, S., Tang, X., and Sun, J. (2015, January 7\u201312). Cascaded hand pose regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298683"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.jvcir.2018.04.005","article-title":"Region Ensemble Network: Towards Good Practices for Deep 3D Hand Pose Estimation","volume":"55","author":"Wang","year":"2018","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Oberweger, M., Wohlhart, P., and Lepetit, V. (2015, January 7\u201313). Training a feedback loop for hand pose estimation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.379"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Malik, M.I., and Liwicki, M. (2012, January 18\u201320). From terminology to evaluation: Performance assessment of automatic signature verification systems. Proceedings of the 2012 International Conference on Frontiers in Handwriting Recognition, Bari, Italy.","DOI":"10.1109\/ICFHR.2012.205"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Nguyen, V., Blumenstein, M., Muthukkumarasamy, V., and Leedham, G. (2007, January 23\u201326). Off-line signature verification using enhanced modified direction features in conjunction with neural classifiers and support vector machines. Proceedings of the Ninth International Conference on Document Analysis and Recognition (ICDAR 2007), Parana, Brazil.","DOI":"10.1109\/ICDAR.2007.4377012"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Malik, M., Liwicki, M., Alewijnse, L., Ohyama, W., Blumenstein, M., and Found, B. (2013, January 25\u201328). Signature verification and writer identification competitions for on-and offline skilled forgeries (sigwicomp2013). Proceedings of the 12th International Conference on Document Analysis and Recognition, Washigton, DC, USA.","DOI":"10.1109\/ICDAR.2013.220"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Guerbai, Y., Chibani, Y., and Abbas, N. (2012, January 10\u201312). One-class versus bi-class SVM classifier for off-line signature verification. Proceedings of the 2012 International Conference on Multimedia Computing and Systems, Tangier, Morocco.","DOI":"10.1109\/ICMCS.2012.6320187"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1016\/j.sigpro.2009.04.043","article-title":"Combining different biometric traits with one-class classification","volume":"89","author":"Bergamini","year":"2009","journal-title":"Signal Process."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Amer, M., Goldstein, M., and Abdennadher, S. (2013, January 11). Enhancing one-class support vector machines for unsupervised anomaly detection. Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, Chicago, IL, USA.","DOI":"10.1145\/2500853.2500857"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1466","DOI":"10.1016\/j.neucom.2006.05.013","article-title":"One-class document classification via neural networks","volume":"70","author":"Manevitz","year":"2007","journal-title":"Neurocomputing"},{"key":"ref_44","first-page":"668","article-title":"Multiple multidimensional sequence alignment using generalized dynamic time warping","volume":"11","author":"Sanguansat","year":"2012","journal-title":"WSEAS Trans. Math."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1109\/TPAMI.2008.32","article-title":"Glove-based approach to online signature verification","volume":"30","author":"Kamel","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Moon, H.C., Jang, S.I., Oh, K., and Toh, K.A. (2017, January 12\u201314). An In-Air Signature Verification System Using Wi-Fi Signals. Proceedings of the 2017 4th International Conference on Biomedical and Bioinformatics Engineering, Seoul, Korea.","DOI":"10.1145\/3168776.3168799"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3872\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:29:03Z","timestamp":1760196543000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3872"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,10]]},"references-count":46,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113872"],"URL":"https:\/\/doi.org\/10.3390\/s18113872","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,10]]}}}