[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128710-en":3,"doc-seo-128710-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128710,962084926284,"Aurora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Effective Facial Expression Recognition System Using Machine Learning","Facial expression recognition (FER) draws extensive attention in computer vision and machine learning, where deep learning has recently improved performance. This work proposes a FER method combining k-nearest neighbours with long short-term memory, using Local Binary Patterns (LBP) for feature extraction. The pipeline separates feature extraction and classification: LBP captures facial texture, while KNN assigns labels and LSTM addresses temporal limitations by modeling sequence dynamics. Experiments on CK+ and Oulu-CASIA show state-of-the-art results with higher F1-score and precision, supporting applications such as human-computer interaction and emotion detection.","EAI Endorsed Transactions  \non Internet of Things Research Article   \nEffective Facial Expression Recognition System Using Machine Learning  \nDheeraj Hebri 1, *, Ramesh Nuthakki2, Ashok Kumar Digal3, K. G. S. Venkatesan4, Sonam Chawla5, CRaghavendra Reddy6  \n1Srinivas Institute of Technology, Mangalore  \n2Atria Institute of Technology, ASKB Campus, 1st main Road, Anand Nagar, RT Nagar, Bangalore  \n3Department of Education, PG Department of Education, Rama Devi Women's University Vidya Vihar Bhubaneswar Odisha India  \n4Department ofC. S. E, MEGHA Institute ofEngg. Tech. for Women, Edulabad-501 301, Hyderabad, Telengana  \n5Department of Organizational Behaviour and Human Resources, Jindal Global Business School, O. P. Jindal Global University, India  \n6SL of English, School of Liberal Arts and Sciences, Mohan Babu University (Erst while Sree Vidyanikethan Engineering College), Tirupati, Andhra Pradesh  \nAbstract  \nThe co Facial expression recognition (FER) is a topic that has seen a lot of study in computer vision and machine learning. In recent years, deep learning techniques have shown remarkable progress on FER tasks. With this abstract, A Novel Is Advised By Us FER method that combines combined use ofk-nearest neighbours and long short-term memory algorithms better efficiency and accurate facial expression recognition. The proposed system features two primary steps—feature extraction and classification—to get results. When extracting features, we extract features from the facial images using the Local Binary Patterns (LBP) algorithm. LBP is a simple yet powerful feature extraction technique that captures texture information from the image. In the classification stage, we use the KNN and LSTM algorithms for facial expression recognition. KNN is a simple and effective classification algorithm that finds the k closest to the given value neighbours to the test training-set-sample and assigning it to the class that is most frequent among its neighbours. However, KNN has limitations in handling temporal information. To address this limitation, we propose to use LSTM, which is a subclass of RNNs capable of capturing temporal relationships in time series data. The LSTM network takes as input the LBP features of a sequence of facial images and processes them through a series of LSTM cells to estimate the ultimate coding of the phrase. We examine the planned and system on two publicly available records: the CK+ and the Oulu-CASIA datasets. According on the experimental findings, the proposed system achieves performance at the cutting edge on both datasets. The proposed system performs better than other state-of-the-art methods, including those that use deep learning systems, quantitatively, in terms ofF1-score and precision.In conclusion, the proposed FER system that combines KNN and LSTM algorithms achieves high accuracy and an F1 score in recognising facial expressions from sequences of images. This system can be used in many contexts, including human-computer interaction, emotion detection, and behaviour analysis.  \nKeywords: Facial Expression Recognition, Machine Learning, K-Nearest Neighbour, Long Short term Memory  \nReceived on 15 December 2023, accepted on 04 March 2024, published on 11 March 2024  \nCopyright © 2024 D. Hebri et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.  \ndoi: 10.4108/eetiot.5362  \n*[Corresponding author. Email:](Corresponding author. Email: dheeraj.h7@gmail.com)[ ](Corresponding author. Email: dheeraj.h7@gmail.com)[dheeraj.h7@gmail.com](Corresponding author. Email: dheeraj.h7@gmail.com)  \n1. Introduction:  \nAn Identifying Feelings from a Face system is a method that employs computer vision techniques to identify and  \nanalyse facial expressions in images or videos. The system detects facial cha","cbCaiuF9HBj1fGvu","https://ap.wps.com/l/cbCaiuF9HBj1fGvu","pdf",1218976,1,6,"English","en",105,"# Introduction\n## Facial expression recognition overview\n## Motivation and applications","[{\"question\":\"What system design does the proposed FER method use?\",\"answer\":\"It follows two main steps: feature extraction and classification. LBP extracts features from facial images, and KNN and LSTM perform classification.\"},{\"question\":\"Why combine KNN with LSTM in facial expression recognition?\",\"answer\":\"KNN is effective for classification, but it struggles with temporal information. LSTM is used to capture temporal relationships across image sequences.\"},{\"question\":\"Which datasets are used to evaluate the system, and what outcomes are reported?\",\"answer\":\"The method is evaluated on CK+ and Oulu-CASIA. Results indicate cutting-edge performance with improved F1-score and precision compared with state-of-the-art approaches.\"}]","Effective Facial Expression Recognition System Using Machine Learning | PDF",1786002792,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"effective-facial-expression-recognition-system-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/effective-facial-expression-recognition-system-using-machine-learning/128710/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What system design does the proposed FER method use?","Question",{"text":76,"@type":77},"It follows two main steps: feature extraction and classification. LBP extracts features from facial images, and KNN and LSTM perform classification.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Why combine KNN with LSTM in facial expression recognition?",{"text":81,"@type":77},"KNN is effective for classification, but it struggles with temporal information. LSTM is used to capture temporal relationships across image sequences.",{"name":83,"@type":74,"acceptedAnswer":84},"Which datasets are used to evaluate the system, and what outcomes are reported?",{"text":85,"@type":77},"The method is evaluated on CK+ and Oulu-CASIA. Results indicate cutting-edge performance with improved F1-score and precision compared with state-of-the-art approaches.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]