{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:56:40Z","timestamp":1777705000638,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>To assess non-verbal reactions to commodities, services, or products, sentiment analysis is the technique of identifying exhibited human emotions utilizing artificial intelligence-based technology. The facial muscles flex and contract differently in response to each facial expression that a person makes, which facilitates the deep learning AI algorithms\u2019 ability to identify an emotion. Facial emotion analysis has numerous applications across various industries and domains, leveraging the understanding of human emotions conveyed through facial expressions, so it is very much required in healthcare, security and survelliance, Forensics, Autism and cultural studies etc,.. In this study, facially expressed sentiments in real-time photographs as well as in an existing dataset are classified using object detection techniques based on deep learning. Fast Region-based Convolution Neural Network (R-CNN) is an object detection system that uses suggested areas to categorize facial expressions of emotion in real-time. Using a high-quality video collection made up of 24 actors who were photographed facially expressing eight distinct emotions (Happy, Sad, Disgust, Anger, Surprise, Fear, Contempt and Neutral). The Fast R-CNN and Mouth region-based feature extraction and Maximally Stable Extremal Regions (MSER) method used for classification and feature extraction respectively. In order to assess the deep network\u2019s performance, the proposed work builds a confusion matrix. The network generalizes to new images rather well, as seen by the average recognition rate of 97.6% for eight emotions. The suggested deep network approach may deliver superior recognition performance when compared to CNN and SVM methods, and it can be applied to a variety of applications including online classrooms, video game testing, healthcare sectors, and automated industry.<\/jats:p>","DOI":"10.3233\/jifs-233842","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:14:06Z","timestamp":1694776446000},"page":"10141-10155","source":"Crossref","is-referenced-by-count":0,"title":["EmotionFusion: A unified ensemble R-CNN approach for advanced facial emotion analysis"],"prefix":"10.1177","volume":"45","author":[{"given":"A.","family":"Umamageswari","sequence":"first","affiliation":[{"name":"Department of CSE, SRM Institute of Science and Technology, Ramapuram Campus, Chennai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Deepa","sequence":"additional","affiliation":[{"name":"Department of CSE, SRM Institute of Science and Technology, Ramapuram Campus, Chennai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Bhagyalakshmi","sequence":"additional","affiliation":[{"name":"Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"A.","family":"Sangari","sequence":"additional","affiliation":[{"name":"Department of EEE, Rajalakshmi Engineering College, Thandalam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Raja","sequence":"additional","affiliation":[{"name":"Department of CSE, SRM Institute of Science and Technology, Ramapuram Campus, Chennai"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-233842_ref3","doi-asserted-by":"crossref","unstructured":"Sivapatham D. , Arasakumaran U. , Annappan B. and Chandrasekaran S. , Analysis of genetic face images with respect to reflexology for prediction of diseases, Traitement du Signal 40(1) (2023).","DOI":"10.18280\/ts.400102"},{"issue":"2","key":"10.3233\/JIFS-233842_ref4","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.icte.2021.08.019","article-title":"A novel fuzzy C-means based chameleon swarm algorithm for segmentation and progressive neural architecture search for plant disease classification","volume":"9","author":"Umamageswari","year":"2023","journal-title":"ICT Express"},{"key":"10.3233\/JIFS-233842_ref5","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/TSA.2005.860851","article-title":"New insights into the noise reduction Wiener filter,","volume":"14","author":"Chen","year":"2006","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"10.3233\/JIFS-233842_ref6","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection,","volume":"57","author":"Viola","year":"2004","journal-title":"International Journal of Computer Vision"},{"key":"10.3233\/JIFS-233842_ref9","doi-asserted-by":"crossref","first-page":"128","DOI":"10.5201\/ipol.2014.104","article-title":"An analysis of the viola-jones face detection algorithm,","volume":"4","author":"Wang","year":"2014","journal-title":"Image Process Line"},{"issue":"12","key":"10.3233\/JIFS-233842_ref10","first-page":"440","article-title":"Classification of human facial expression based on mouth feature using SUSAN edge operator","volume":"4","author":"Prasad","year":"2014","journal-title":"Int J Adv Res Comput Sci Softw Eng"},{"issue":"10","key":"10.3233\/JIFS-233842_ref11","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/j.imavis.2004.02.006","article-title":"Robust wide-baseline stereo from maximally stable extremal regions","volume":"22","author":"Matas","year":"2004","journal-title":"Image and Vision Computing"},{"key":"10.3233\/JIFS-233842_ref19","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1152\/jn.1987.58.6.1233","article-title":"An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex,","volume":"58","author":"Jones","year":"2017","journal-title":"Journal of Neurophysiology"},{"key":"10.3233\/JIFS-233842_ref21","doi-asserted-by":"crossref","first-page":"117","DOI":"10.7763\/IJCCE.2012.V1.33","article-title":"Facial expression recognition based on image feature,","volume":"1","author":"Kumbhar","year":"2012","journal-title":"International Journal of Computer and Communication Engineering"},{"key":"10.3233\/JIFS-233842_ref22","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1109\/12.210173","article-title":"der Malsburg and R.P. 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