[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-149273-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-149273-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","real-time-emotion-detection-in-students-with-hybrid-deep-learning-architecture-using-resnet-and-zfnet","Real Time Emotion Detection in Students with Hybrid Deep Learning Architecture Using Resnet and Zfnet","","Deep learning enables automated feature extraction and recognition of complex patterns, making it effective for image understanding and sentiment-related tasks. Student motivation strongly influences academic outcomes, yet traditional engagement evaluation relies on subjective observation and self-reported questionnaires. This paper presents a real-time sentiment detection system using a hybrid deep learning architecture that combines ZFNet and ResNet to identify facial expressions. Trained on the EmotiW2019 dataset, it maps student emotions to engagement classes (engaged vs not engaged), processes live webcam input, and delivers instant visual feedback with emojis and engagement metrics.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/real-time-emotion-detection-in-students-with-hybrid-deep-learning-architecture-using-resnet-and-zfnet/149273/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/real-time-emotion-detection-in-students-with-hybrid-deep-learning-architecture-using-resnet-and-zfnet/149273.png","ImageObject",300,407,{"name":42,"@type":43},"Sage","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-31","2026-08-27",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",12,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"How does the proposed system measure student engagement in real time?","Question",{"text":63,"@type":64},"It uses webcam-based facial expression analysis to infer engagement-related emotions, then classifies them into categories such as “engaged” or “not engaged”.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"What hybrid architecture is used for emotion recognition?",{"text":68,"@type":64},"The system combines ZFNet and ResNet to improve feature extraction and classification efficiency in a real-time setting.",{"name":70,"@type":61,"acceptedAnswer":71},"Which dataset trains and evaluates the model?",{"text":72,"@type":64},"The model is trained using the EmotiW2019 dataset for facial emotion and engagement-related classification.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},149273,1787797376,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":140,"language":141,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":12,"update_tm":80,"read_time":145,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Real Time Emotion Detection in Students with Hybrid Deep Learning Architecture Using  \nResnet and Zfnet  \nMs. K. Thrilochana Devi 1, Uppala Bharath Kumar2, Bakka Tarun Kumar3, Ravula Rajeev Chowdary4, Rachamandi Sai Siva Teja5  \n1Assistant Professor  \n1, 2, 3, 4, 5Dept of Information Technology, Vasireddy Venkatadri Institute of Technology Guntur, India  \nAbstract  \nThe realm of deep learning facilitates the automated extraction of features and the recognition of intricate patterns, rendering it exceptionally proficient for complicated tasks such as image categorization, natural language analysis, and sentiment recognition. Student motivation plays a pivotal role in achieving academic success; however, conventional techniques for evaluating engagement are rooted in subjective assessments. This Paper proposes a real-time sentiment detection system that harnesses a hybrid deep learning structure that fuses ZFNet and ResNet for precise facial expression identification. By utilizing the EmotiW2019 dataset, the model sorts student emotions into engagement-related classifications like \"engaged\" or \"not engaged.\" The system interprets live webcam input, offering instant visual feedback through emojis and metricson engagement. By merging cutting-edge computer vision and deep learning methodologies, this adaptable solution amplifies both physical and online learning settings, empowering datainformed interventions to enhance student involvement and educational outcomes. Experimental findings reveal impressive classification precision, instantaneous performance, and practical applicability in academic contexts.  \nKeywords: Emotion Detection, Deep Learning, ResNet, ZFNet, Real-Time Processing, Student Engagement, Computer Vision  \nINTRODUCTION  \nStudent motivation is a vital element impacting academic achievement; nevertheless, traditional approaches to measuring engagement depend on manual observation and self-reported questionnaires that are often subjective and inefficient. With advancements in deep learning and computer vision, automated recognition of emotions through facial expressions has surfaced as a promising method for assessing engagement levels in educational environments. Deep learning models possess the capability to evaluate facial features to categorize emotions, generating real-time insights into student motivation levels. However, current models encounter obstacles in attaining high precision and efficiency, particularly in realtime scenarios. The paper at hand introduces a real-time emotion recognition system that utilizes a hybrid deep learning structure, melding ZFNet and ResNet to enhance feature extraction  \nand classification efficiency.  \nTrained on the EmotiW2019 dataset, the model classifies student emotions based on engagement metrics, such as \"engaged\" or \"not engaged.\" By scrutinizing facial expressions obtained via a webcam, the system processes the input and provides immediate feedback using visual indicators like emojis and engagement ratings. This flexible and non-invasive method permits ongoing surveillance of student participation in both physical and virtual learning conditions, facilitating tailored interventions to enhance educational experiences.  \nLITERATURE REVIEW  \nFacial emotion recognition (FER) has emerged as a highly researched topic within artificial intelligence, with marked advancements fueled by deep learning techniques. Traditional methodologies relied on custom-feature extraction approaches like Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG), which were frequently hindered by their failure to capture intricate facial variations. With the introduction of Convolutional Neural Networks (CNNs), models such as VGG16, MobileNet, and InceptionNet have shown considerable advancements in classifying facial expressions. These architectures utilize profound hierarchical feature extraction yet often struggle with generalization across varied datasets, resulting in inconsiste","cbCaiaWRH9dAnBeA","https://ap.wps.com/l/cbCaiaWRH9dAnBeA","pdf",758045,13,"English","# Introduction\n# Literature Review","[{\"question\":\"How does the proposed system measure student engagement in real time?\",\"answer\":\"It uses webcam-based facial expression analysis to infer engagement-related emotions, then classifies them into categories such as “engaged” or “not engaged”.\"},{\"question\":\"What hybrid architecture is used for emotion recognition?\",\"answer\":\"The system combines ZFNet and ResNet to improve feature extraction and classification efficiency in a real-time setting.\"},{\"question\":\"Which dataset trains and evaluates the model?\",\"answer\":\"The model is trained using the EmotiW2019 dataset for facial emotion and engagement-related classification.\"}]","Real Time Emotion Detection in Students with Hybrid Deep Learning Architecture Using Resnet and Zfnet | PDF",33]