{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:14:10Z","timestamp":1780355650416,"version":"3.54.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"28","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1007\/s11042-023-15537-0","type":"journal-article","created":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T07:01:19Z","timestamp":1682924479000},"page":"44227-44244","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spatiotemporal fusion personality prediction based on visual information"],"prefix":"10.1007","volume":"82","author":[{"given":"Jia","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijian","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoyun","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangyu","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,1]]},"reference":[{"key":"15537_CR1","doi-asserted-by":"crossref","unstructured":"Attrapadung N, Hamada K, Ikarashi D, Kikuchi R, Matsuda T, Mishina I, Morita H, Schuldt J (2021) Adam in Private: Secure and Fast Training of Deep Neural Networks with Adaptive Moment Estimation.","DOI":"10.56553\/popets-2022-0131"},{"key":"15537_CR2","unstructured":"Brooks J (2011) Asdarepro deal forSun and Imagenet[J]. Packaging News, p.3"},{"issue":"5","key":"15537_CR3","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1109\/TFUZZ.2014.2370678","volume":"23","author":"X Cao","year":"2015","unstructured":"Cao X, Liu Z (2015) Type-2 Fuzzy Topic Models for Human Action Recognition. IEEE Trans Fuzzy Syst 23(5):1581\u20131593. https:\/\/doi.org\/10.1109\/TFUZZ.2014.2370678","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"15537_CR4","unstructured":"Diba A, Pazandeh AM, Gool LV (2016) Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification[J]"},{"key":"15537_CR5","doi-asserted-by":"publisher","unstructured":"Hara K, Kataoka H and Satoh Y (2018) Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?, 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6546-6555, doi: https:\/\/doi.org\/10.1109\/CVPR.2018.00685","DOI":"10.1109\/CVPR.2018.00685"},{"key":"15537_CR6","doi-asserted-by":"publisher","unstructured":"Hara K, Kataoka H and Satoh Y (2018) Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?,\"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6546-6555, doi: https:\/\/doi.org\/10.1109\/CVPR.2018.00685","DOI":"10.1109\/CVPR.2018.00685"},{"key":"15537_CR7","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?[J]","DOI":"10.1109\/CVPR.2018.00685"},{"key":"15537_CR8","doi-asserted-by":"publisher","unstructured":"Joo J, Steen FF, Zhu S-C (2015) Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face, 2015 IEEE International Conference on Computer Vision (ICCV), pp. 3712-3720, doi: https:\/\/doi.org\/10.1109\/ICCV.2015.423","DOI":"10.1109\/ICCV.2015.423"},{"key":"15537_CR9","doi-asserted-by":"publisher","first-page":"102430","DOI":"10.1016\/j.media.2022.102430","volume":"79","author":"QH Lin","year":"2022","unstructured":"Lin QH, Niu YW, Sui J et al (2022) SSPNet: An interpretable 3D-CNN for classification of schizophrenia using phase maps of resting-state complex-valued fMRI data[J]. Med Image Anal 79:102430","journal-title":"Med Image Anal"},{"issue":"1","key":"15537_CR10","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/TFUZZ.2020.3006520","volume":"29","author":"S Liu","year":"2021","unstructured":"Liu S, Wang S, Liu X, Lin C-T, Lv Z (2021) Fuzzy Detection Aided Real-Time and Robust Visual Tracking Under Complex Environments. IEEE Trans Fuzzy Syst 29(1):90\u2013102. https:\/\/doi.org\/10.1109\/TFUZZ.2020.3006520","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"15537_CR11","doi-asserted-by":"publisher","first-page":"2188","DOI":"10.1109\/TMM.2021.3065580","volume":"23","author":"S Liu","year":"2021","unstructured":"Liu S et al (2021) Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring. IEEE Trans Multimedia 23:2188\u20132198. https:\/\/doi.org\/10.1109\/TMM.2021.3065580","journal-title":"IEEE Trans Multimedia"},{"issue":"3","key":"15537_CR12","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/T-AFFC.2012.5","volume":"3","author":"G Mohammadi","year":"2012","unstructured":"Mohammadi G, Vinciarelli A (2012) Automatic Personality Perception: Prediction of Trait Attribution Based on Prosodic Features. IEEE Trans Affective Comput 3(3):273\u2013284. https:\/\/doi.org\/10.1109\/T-AFFC.2012.5","journal-title":"IEEE Trans Affective Comput"},{"issue":"7","key":"15537_CR13","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.1109\/TMM.2016.2557058","volume":"18","author":"LS Nguyen","year":"2016","unstructured":"Nguyen LS, Gatica-Perez D (2016) Hirability in the wild: Analysis of online conversational video resumes. IEEE Trans Multimedia 18(7):1422\u20131437","journal-title":"IEEE Trans Multimedia"},{"key":"15537_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-49409-8_32","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science()","author":"V Ponce-L\u00f3pez","year":"2016","unstructured":"Ponce-L\u00f3pez V et al (2016) ChaLearn LAP 2016: First Round Challenge on First Impressions - Dataset and Results. In: Hua G, J\u00e9gou H (eds) Computer Vision \u2013 ECCV 2016 Workshops. ECCV 2016. Lecture Notes in Computer Science(), vol 9915. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-49409-8_32"},{"key":"15537_CR15","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 et al (2015) ImageNet Large Scale Visual Recognition Challenge. IntJ Comput Vis 115:211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"IntJ Comput Vis"},{"key":"15537_CR16","unstructured":"Sammeta V, Naveen Y, Suresh C (n.d.) Acoustics Recognition and Video Sound-Track Classification using CNN"},{"key":"15537_CR17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/BF01389847","volume":"30","author":"W Schmid","year":"1975","unstructured":"Schmid W (1975) On the characters of the discrete series. Invent Math 30:47\u2013144. https:\/\/doi.org\/10.1007\/BF01389847","journal-title":"Invent Math"},{"issue":"4","key":"15537_CR18","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1109\/TCSVT.2010.2041828","volume":"21","author":"M Teng","year":"2011","unstructured":"Teng M, Tao et al (2011) Contextual Bag-of-Words for Visual Categorization.[J]. IEEE Trans Circuits Syst Video Technol 21(4):381\u2013392","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"15537_CR19","doi-asserted-by":"publisher","first-page":"4489","DOI":"10.1109\/ICCV.2015.510","volume":"2015","author":"D Tran","year":"2015","unstructured":"Tran D, Bourdev L, Fergus R, Torresani L, Paluri M (2015) Learning Spatiotemporal Features with 3D Convolutional Networks. IEEE Int Conference Comput Vision (ICCV) 2015:4489\u20134497. https:\/\/doi.org\/10.1109\/ICCV.2015.510","journal-title":"IEEE Int Conference Comput Vision (ICCV)"},{"issue":"10","key":"15537_CR20","doi-asserted-by":"publisher","first-page":"7128","DOI":"10.1109\/JIOT.2021.3077600","volume":"9","author":"S Wang","year":"2022","unstructured":"Wang S et al (2022) Human Short Long-Term Cognitive Memory Mechanism for Visual Monitoring in IoT-Assisted Smart Cities. IEEE Internet Things J 9(10):7128\u20137139. https:\/\/doi.org\/10.1109\/JIOT.2021.3077600","journal-title":"IEEE Internet Things J"},{"issue":"3","key":"15537_CR21","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1109\/TAFFC.2017.2762299","volume":"9","author":"X Wei","year":"2018","unstructured":"Wei X, Zhang C, Zhang H, Wu J (2018) Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences. IEEE Trans Affect Comput 9(3):303\u2013315. https:\/\/doi.org\/10.1109\/TAFFC.2017.2762299","journal-title":"IEEE Trans Affect Comput"},{"key":"15537_CR22","doi-asserted-by":"crossref","unstructured":"Wolf L, Levy N (2013) The SVM-Minus Similarity Score for Video Face Recognition[C]\/\/ IEEE Conference on Computer Vision & Pattern Recognition. IEEE","DOI":"10.1109\/CVPR.2013.452"},{"key":"15537_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00021-9_54","volume-title":"Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science","author":"J Xu","year":"2018","unstructured":"Xu J, Tian W, Fan Y, Lin Y, Zhang C (2018) Personality Trait Prediction Based on 2.5D Face Feature Model. In: Sun X, Pan Z, Bertino E (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science, vol 11068. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-00021-9_54"},{"key":"15537_CR24","doi-asserted-by":"publisher","first-page":"76822","DOI":"10.1109\/ACCESS.2021.3076989","volume":"9","author":"J Xu","year":"2021","unstructured":"Xu J, Tian W, Lv G, Liu S, Fan Y (2021) Prediction of the Big Five Personality Traits Using Static Facial Images of College Students With Different Academic Backgrounds. IEEE Access 9:76822\u201376832. https:\/\/doi.org\/10.1109\/ACCESS.2021.3076989","journal-title":"IEEE Access"},{"key":"15537_CR25","doi-asserted-by":"publisher","first-page":"5581984","DOI":"10.1155\/2021\/5581984","volume":"2021","author":"J Xu","year":"2021","unstructured":"Xu J, Tian W, Lv G, Liu S, Fan Y (2021) 2.5D Facial Personality Prediction Based on Deep Learning. J Adv Trans 2021:5581984, 12 pages. https:\/\/doi.org\/10.1155\/2021\/5581984","journal-title":"J Adv Trans"},{"key":"15537_CR26","unstructured":"Yan S (2014) Some examples from Caltech101\/256 and PASCAL VOC 2007\/2011 datasets"},{"key":"15537_CR27","doi-asserted-by":"publisher","first-page":"9127","DOI":"10.1609\/aaai.v33i01.33019127","volume":"33","author":"Z Yu","year":"2019","unstructured":"Yu Z, Xu D, Yu J, Yu T, Zhao Z, Zhuang Y, Tao D (2019) ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering. Proc AAAI Conference Artificial Intell 33:9127\u20139134. https:\/\/doi.org\/10.1609\/aaai.v33i01.33019127","journal-title":"Proc AAAI Conference Artificial Intell"},{"key":"15537_CR28","doi-asserted-by":"crossref","unstructured":"Zha S, Luisier F, Andrews W, Srivastava N and Salakhutdinov R (2015) Exploiting Image-trained CNN Architectures for Unconstrained Video Classification. In Xianghua Xie, Mark W. Jones, and Gary K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), pages 60.1-60.13. BMVA Press","DOI":"10.5244\/C.29.60"},{"key":"15537_CR29","doi-asserted-by":"publisher","unstructured":"Zhang W and Wu Y (2022) Semantic sentiment analysis based on a combination of CNN and LSTM model in 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE), Guilin, China, pp. 177-180.doi:https:\/\/doi.org\/10.1109\/MLKE55170.2022.00041","DOI":"10.1109\/MLKE55170.2022.00041"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15537-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15537-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15537-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T06:11:42Z","timestamp":1698473502000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15537-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":29,"journal-issue":{"issue":"28","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["15537"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15537-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"assertion":[{"value":"14 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 April 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 May 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Participants were asked for oral consent to participate in the study, and all data were collected after obtaining consent. The data from consenting participants were applied in this study. In addition, we numbered each subject, and the self-reported personality assessment data were collected anonymously in the form of numbers.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}