{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:20:57Z","timestamp":1771024857158,"version":"3.50.1"},"reference-count":55,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,1,30]]},"abstract":"<jats:p>The interest in Facial Expression Recognition (FER) is increasing day by day due to its practical and potential applications, such as human physiological interaction diagnosis and mental disease detection. This area has received much attention from the research community in recent years and achieved remarkable results; however, a significant improvement is required in spatial problems. This research work presents a novel framework and proposes an effective and robust solution for FER under an unconstrained environment; it also helps us to classify facial images in the client\/server model along with preserving privacy. There are a lot of cryptography techniques available but they are computationally expensive; on the other side, we have implemented a lightweight method capable of ensuring secure communication with the help of randomization. Initially, we perform preprocessing techniques to encounter the unconstrained environment. Face detection is performed for the removal of excessive background and it detects the face in the real-world environment. Data augmentation is for the insufficient data regime. A dual-enhanced capsule network is used to handle the spatial problem. The traditional capsule networks are unable to sufficiently extract the features, as the distance varies greatly between facial features. Therefore, the proposed network is capable of spatial transformation due to the action unit aware mechanism and thus forwards the most desiring features for dynamic routing between capsules. The squashing function is used for classification purposes. Simple classification is performed through a single party, whereas we also implemented the client\/server model with privacy measurements. Both parties do not trust each other, as they do not know the input of each other. We have elaborated that the effectiveness of our method remains unchanged by preserving privacy by validating the results on four popular and versatile databases that outperform all the homomorphic cryptographic techniques.<\/jats:p>","DOI":"10.1155\/2021\/6673992","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T19:05:21Z","timestamp":1612033521000},"page":"1-12","source":"Crossref","is-referenced-by-count":11,"title":["Fusion of Machine Learning and Privacy Preserving for Secure Facial Expression Recognition"],"prefix":"10.1155","volume":"2021","author":[{"given":"Asad","family":"Ullah","sequence":"first","affiliation":[{"name":"Department of Computer Science & IT, Sarhad University of Science and Information Technology, Peshawar, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3653-9951","authenticated-orcid":true,"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Shahid","family":"Anwar","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3576-8365","authenticated-orcid":true,"given":"Arshad","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of IT & Computer Science, Pak-Austria Fachhoschule: Institute of Applied Sciences and Technology, Haripur, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0126-9944","authenticated-orcid":true,"given":"Shah","family":"Nazir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Swabi, Swabi, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8373-2781","authenticated-orcid":true,"given":"Habib Ullah","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Accounting & Information Systems, College of Business & Economics, Qatar University, Doha, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zesong","family":"Fei","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.3390\/s19081863"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"key":"3","first-page":"490","article-title":"Visual attention in schizophrenia: eye contact and gaze aversion during clinical interactions","author":"A. K. Vail"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8121487"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1037\/h0030377"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2015.08.011"},{"key":"7","first-page":"418","article-title":"Feature extraction based on canonical correlation analysis using FMEDA and DPA for facial expression recognition with RNN","author":"A. Ullah"},{"key":"8","article-title":"Identity-free facial expression recognition using conditional generative adversarial network","author":"J. Cai","year":"2019"},{"key":"9","first-page":"294","article-title":"Identity-adaptive facial expression recognition through expression regeneration using conditional generative adversarial networks","author":"H. Yang"},{"key":"10","first-page":"1","article-title":"CapsuleNet for micro-expression recognition","author":"N. Van Quang"},{"key":"11","article-title":"Depression: key facts","author":"W. H. Organization","year":"2018"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2008.06.026"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.04.002"},{"key":"14","first-page":"84","article-title":"Multi-region ensemble convolutional neural network for facial expression recognition","author":"Y. Fan"},{"key":"15","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TIP.2018.2868382","article-title":"Reliable crowdsourcing and deep locality-preserving learning for unconstrained facial expression recognition","volume":"28","author":"S. Li","year":"2018","journal-title":"IEEE Transactions on Image Processing"},{"key":"16","article-title":"A spatio-temporal descriptor based on 3d-gradients","author":"A. Klaser"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2009.2024578"},{"key":"18","first-page":"314","article-title":"Action unit detection using sparse appearance descriptors in space-time video volumes","author":"B. Jiang"},{"key":"19","first-page":"143","article-title":"Deeply learning deformable facial action parts model for dynamic expression analysis","author":"M. Liu"},{"key":"20","first-page":"427","article-title":"Hierarchical committee of deep cnns with exponentially-weighted decision fusion for static facial expression recognition","author":"B. K. Kim"},{"key":"21","first-page":"443","article-title":"Deep learning for emotion recognition on small datasets using transfer learning","author":"H. W. Ng"},{"key":"22","first-page":"435","article-title":"Image based static facial expression recognition with multiple deep network learning","author":"Z. Yu"},{"key":"23","first-page":"2852","article-title":"Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild","author":"S. Li"},{"key":"24","first-page":"558","article-title":"Identity-aware convolutional neural network for facial expression recognition","author":"Z. Meng"},{"key":"25","first-page":"302","article-title":"Island loss for learning discriminative features in facial expression recognition","author":"J. Cai"},{"key":"26","first-page":"2168","article-title":"Facial expression recognition by de-expression residue learning","author":"H. Yang"},{"key":"27","article-title":"Facial expression recognition using human to animated-character expression translation","author":"K. Ali","year":"2019"},{"key":"28","first-page":"235","article-title":"Privacy-preserving face recognition","author":"Z. Erkin"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1109\/34.598228"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1109\/t-affc.2012.33"},{"key":"31","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/3-540-48910-X_16","article-title":"Public-key cryptosystems based on composite degree residuosity classes","volume-title":"Advances in Cryptology EUROCRYPT 99","author":"P. Paillier","year":"1999"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2016.2573770"},{"key":"33","article-title":"Privacy-preserving object detection for medical images with faster R-CNN","author":"Y. Liu"},{"key":"34","article-title":"Rapid object detection using a boosted cascade of simple features","author":"P. Viola"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2018.2791608"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1949.tb00928.x"},{"key":"37","article-title":"Secure multiparty computation","author":"O. Goldreich","year":"1998"},{"key":"38","article-title":"The Extended Cohn-Kanade Dataset (CK+): a complete dataset for action unit and emotion-specified expression","author":"P. Lucey"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1109\/icme.2005.1521424"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2011.07.002"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2007.1110"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2013.03.003"},{"key":"43","article-title":"Learning expressionlets on spatio-temporal manifold for dynamic facial expression recognition","author":"M. Liu"},{"key":"44","doi-asserted-by":"crossref","article-title":"Joint fine-tuning in deep neural networks for facial expression recognition","author":"H. Jung","DOI":"10.1109\/ICCV.2015.341"},{"key":"45","article-title":"Deeply learning deformable facial action parts model for dynamic expression analysis","author":"M. Liu"},{"key":"46","first-page":"1","article-title":"Nonlinear manifold feature extraction based on spectral supervised canonical correlation analysis for facial expression recognition with RRNN","author":"A. Ullah"},{"key":"47","first-page":"1524","article-title":"Facial expression recognition for different pose faces based on special landmark detection","author":"W. Wu"},{"key":"48","first-page":"2562","article-title":"Learning active facial patches for expression analysis","author":"L. Zhong"},{"key":"49","first-page":"631","article-title":"Dynamic facial expression recognition using longitudinal facial expression atlases","author":"Y. Guo"},{"key":"50","article-title":"FaceNet2ExpNet: regularizing a deep face recognition net for expression recognition","author":"H. Ding","year":"2016"},{"key":"51","article-title":"Peak-piloted deep network for facial expression recognition","author":"X. Zhao"},{"key":"52","article-title":"Auxiliary image regularization for deep cnns with noisy labels","author":"S. Azadi","year":"2015"},{"key":"53","article-title":"Training deep neural-networks using a noise adaptation layer","author":"J. Goldberger","year":"2016"},{"key":"54","doi-asserted-by":"publisher","DOI":"10.2307\/2346806"},{"key":"55","first-page":"222","article-title":"Facial expression recognition with inconsistently annotated datasets","author":"J. Zeng"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6673992.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6673992.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6673992.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T19:05:24Z","timestamp":1612033524000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2021\/6673992\/"}},"subtitle":[],"editor":[{"given":"Amir","family":"Anees","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,1,30]]},"references-count":55,"alternative-id":["6673992","6673992"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6673992","relation":{},"ISSN":["1939-0122","1939-0114"],"issn-type":[{"value":"1939-0122","type":"electronic"},{"value":"1939-0114","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1,30]]}}}