{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:52:27Z","timestamp":1777697547235,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2023,5,15]]},"abstract":"<jats:p>The Graphic Interchange Format (GIF) is a bitmap picture format that has a series of perpetually repeating images or silent movies that may be viewed without the user having to click and start them. GIFs are frequently used to visually represent emotions that are expressed through body language such as gestures, movements, and facial expressions. Computing may be used to recognise thoughts and other emotions like desire, interest, sentiments, etc. by using emotional expressions or movements as face markers or properties in GIFs. The ability to predict emotions in GIFs may make it easier to express oneself on social media and convey a person\u2019s attitude or personality. Emotion detection in GIFs may be utilised for a range of purposes, e.g., developing a recommendation system, detecting inappropriate content, sentiment identification from GIF-induced sentiment as perceived by person and creating a GIF tag generating system. This study discusses the prior contributions made towards emotion identification in GIFs and describes a method for detecting seven different emotion classes (Happy, Anger, Sad, Surprise, Disgust, Fear, and Neutral) in GIFs by combining an activity recognition network with face emotional expression. The suggested deep neural network, RNN, LSTM approach produced an F1-score of 0.89 and an accuracy of 88 percent.<\/jats:p>","DOI":"10.3233\/idt-220158","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T11:51:18Z","timestamp":1670932278000},"page":"415-433","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced deep learning network for emotion recognition from GIF"],"prefix":"10.1177","volume":"17","author":[{"given":"Agam","family":"Madan","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jolly","family":"Parikh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rachna","family":"Jain","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Bhagwan Parshuram Institute of Technology, Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aryan","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankit","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dhruv","family":"Chadha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Shubham","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDT-220158_ref1","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1145\/2733373.2806284","article-title":"Joint visual-textual sentiment analysis with deep neural networks","author":"You","year":"2015","journal-title":"Proceedings of the 23rd ACM International Conference on Multimedia"},{"issue":"15","key":"10.3233\/IDT-220158_ref2","doi-asserted-by":"crossref","first-page":"8955","DOI":"10.1007\/s11042-014-2337-z","article-title":"Visual sentiment topic model-based microblog image sentiment analysis","volume":"75","author":"Cao","year":"2016","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/IDT-220158_ref3","first-page":"367","article-title":"Predicting perceived emotions in animated GIFs with 3D convolutional neural networks","author":"Chen","year":"2016","journal-title":"Proceedings of the IEEE International Symposium on Multimedia (ISM)"},{"key":"10.3233\/IDT-220158_ref4","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/ICME.2019.00191","article-title":"Human-centred emotion recognition in animated gifs","author":"Yang","year":"2019","journal-title":"Proceedings of the IEEE International Conference on Multimedia and Expo (ICME)"},{"key":"10.3233\/IDT-220158_ref5","doi-asserted-by":"publisher","first-page":"4641","DOI":"10.1109\/CVPR.2016.502","article-title":"TGIF: A New Dataset and Benchmark on Animated GIF Description","author":"Li","year":"2016","journal-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"issue":"1","key":"10.3233\/IDT-220158_ref6","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1609\/aaai.v34i01.5364","article-title":"An End-to-End visual-audio attention network for emotion recognition in user-generated videos","volume":"34","author":"Zhao","year":"2020","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"10.3233\/IDT-220158_ref7","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1145\/2964284.2967196","article-title":"Emotion in context: Deep semantic feature fusion for video emotion recognition","author":"Chen","year":"2016","journal-title":"Proceedings of the 24th ACM International Conference on Multimedia"},{"issue":"4","key":"10.3233\/IDT-220158_ref8","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1109\/TMM.2019.2936805","article-title":"Sentiment Recognition for Short Annotated GIFs Using Visual-Textual Fusion","volume":"22","author":"Liu","year":"2019","journal-title":"IEEE Transactions on Multimedia"},{"key":"10.3233\/IDT-220158_ref9","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"10.3233\/IDT-220158_ref10","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1145\/3340555.3355713","article-title":"Exploring emotion features and fusion strategies for audio-video emotion recognition","author":"Zhou","year":"2019","journal-title":"Proceedings of International Conference on Multimodal Interaction"},{"key":"10.3233\/IDT-220158_ref11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/CVPR.2015.7298594","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015","journal-title":"Proceedings of the IEEE conference on computer vision and pattern recognition"},{"key":"10.3233\/IDT-220158_ref12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1705.06950"},{"issue":"6334","key":"10.3233\/IDT-220158_ref13","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1126\/science.aal4230","article-title":"Semantics derived automatically from language corpora contain humanlike biases","volume":"356","author":"Caliskan","year":"2017","journal-title":"Science"},{"key":"10.3233\/IDT-220158_ref14","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep Residual Learning for Image Recognition","author":"He","year":"2016","journal-title":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"10.3233\/IDT-220158_ref15","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1109\/CVPR.2016.291","article-title":"Actions \u223c Transformations","author":"Wang","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"10.3233\/IDT-220158_ref16","doi-asserted-by":"crossref","first-page":"4489","DOI":"10.1109\/ICCV.2015.510","article-title":"Learning spatiotemporal features with 3d convolutional networks","author":"Tran","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision (ICCV)"},{"key":"10.3233\/IDT-220158_ref17","doi-asserted-by":"publisher","first-page":"4724","DOI":"10.1109\/CVPR.2017.502","article-title":"Quo vadis, Action recognition? A new model and the kinetics dataset","author":"Carreira","year":"2017","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"10.3233\/IDT-220158_ref18","unstructured":"Khurram S, Zamir A, Shah M. UCF101: A Dataset of 101 Human Actions Classes from Videos in The Wild. 2012; arXiv preprint arXiv:1212.0402."},{"key":"10.3233\/IDT-220158_ref19","first-page":"1","article-title":"Understanding cartoon emotion using integrated deep neural network on large dataset","author":"Jain","year":"2021","journal-title":"Neural Computing and Applications"},{"key":"10.3233\/IDT-220158_ref20","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/BF01115465","article-title":"Measuring facial movement","volume":"1","author":"Ekman","year":"1976","journal-title":"J Nonverbal Behaviour"},{"key":"10.3233\/IDT-220158_ref21","doi-asserted-by":"crossref","unstructured":"Shivhare S, Khethawat S. Emotion Detection from Text. 2012; arXiv preprint arXiv:1205.4944.","DOI":"10.5121\/csit.2012.2237"},{"issue":"5","key":"10.3233\/IDT-220158_ref22","doi-asserted-by":"crossref","first-page":"3297","DOI":"10.3233\/JIFS-169272","article-title":"Movie Prism: A novel system for aspect level sentiment profiling of movies","volume":"32","author":"Piryani","year":"2017","journal-title":"J Intelligent Fuzzy System"},{"key":"10.3233\/IDT-220158_ref23","first-page":"2373","article-title":"Emotion detection algorithm using frontal face image","author":"Kim","year":"2005","journal-title":"International Conference on Control and Robotics Systems"},{"key":"10.3233\/IDT-220158_ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06012-8"},{"key":"10.3233\/IDT-220158_ref25","doi-asserted-by":"crossref","unstructured":"Abinaya R, Lakshmana P, Maguluri, Narayana S, Syamala M. A Novel Biometric Approach for Facial Image Recognition Using Deep Learning Techniques. International Journal of Advanced Research in Engineering and Technology. 2020; 11(9).","DOI":"10.30534\/ijatcse\/2020\/283952020"},{"key":"10.3233\/IDT-220158_ref26","first-page":"1","article-title":"Facial emotion recognition in the elderly using a SVM classifier","author":"Lopes","year":"2018","journal-title":"2nd International Conference on Technology and Innovation in Sports, Health and Wellbeing (TISHW)"},{"key":"10.3233\/IDT-220158_ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICSSS49621.2020.9202053"},{"key":"10.3233\/IDT-220158_ref28","doi-asserted-by":"publisher","first-page":"417","DOI":"10.3233\/IDT-190101","article-title":"Human Emotion Recognition Using Intelligent Approaches: A Review","author":"Chowdary","year":"2019","journal-title":"Intelligent Decision Technologies"},{"key":"10.3233\/IDT-220158_ref29","first-page":"89","article-title":"Facial Expression Detection Model of Seven Expression Types Using Hybrid Feature Selection and Deep CNN","author":"Srinivas","year":"2020","journal-title":"International Conference on Intelligent and Smart Computing in Data Analytics: ISCDA"},{"key":"10.3233\/IDT-220158_ref30","first-page":"71","article-title":"Emotion Recognition Using Feature Extraction Techniques","author":"Chowdary","year":"2020","journal-title":"Information Technology and Intelligent Transportation Systems"},{"key":"10.3233\/IDT-220158_ref31","first-page":"419","article-title":"Deep Learning approach for text, age, and GIF multimodal sentiment analysis","author":"Shirzad","year":"2020","journal-title":"Proceedings of 10th International Conference on Computer and Knowledge Engineering (ICCKE)"},{"key":"10.3233\/IDT-220158_ref32","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1109\/ACII.2017.8273647","article-title":"Gifgif+: Collecting emotional animated gifs with clustered multi-task learning","author":"Chen","year":"2017","journal-title":"2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII)"},{"key":"10.3233\/IDT-220158_ref33","first-page":"575","article-title":"Fast, cheap, and good: Why animated gifs engage us in CHI","author":"Bakhshi","year":"2016","journal-title":"Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI \u201916)"},{"issue":"40","key":"10.3233\/IDT-220158_ref34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.17485\/ijst\/2016\/v9i40\/99425","article-title":"Performance evaluation of classification algorithms on different datasets","volume":"9","author":"Gupta","year":"2016","journal-title":"Indian Journal of Science"},{"key":"10.3233\/IDT-220158_ref35","first-page":"395","article-title":"Happy dance, Slow Clap: Using Reaction GIFs to Predict Induced Affected on Twitter","volume":"2","author":"Boaz","year":"2021","journal-title":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing"},{"key":"10.3233\/IDT-220158_ref36","doi-asserted-by":"crossref","unstructured":"Jou B, Bhattacharya S, Chang S. Predicting Viewer Perceived emotions in animated GIFs. In: Proceedings of conference of ACM Multimedia, Orlando, FL, USA: November 2014.","DOI":"10.1145\/2647868.2656408"},{"key":"10.3233\/IDT-220158_ref37","unstructured":"Sanjeevi M. Chapter 10.1: DeepNLP\u00a0\u2013 LSTM (Long Short Term Memory) Networks with Math. Deep Math Machine learning.ai. January 2018; Accessed on 13\/08\/2022. Available from: https:\/\/medium.com\/deep-math-machine-learning-ai\/tagged\/artificial-intelligence."}],"container-title":["Intelligent Decision Technologies"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDT-220158","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:23:31Z","timestamp":1777454611000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDT-220158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":37,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/idt-220158","relation":{},"ISSN":["1872-4981","1875-8843"],"issn-type":[{"value":"1872-4981","type":"print"},{"value":"1875-8843","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,15]]}}}