{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T20:40:51Z","timestamp":1775421651043,"version":"3.50.1"},"reference-count":46,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T00:00:00Z","timestamp":1643760000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["42-FTTJ-38"],"award-info":[{"award-number":["42-FTTJ-38"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2022,2,2]]},"abstract":"<jats:p>Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and visual modalities. Facial expressions can be considered as ideal means for detecting the persons' emotions. This paper presents a real-time approach for implementing emotion detection and deploying it in the robotic vision applications. The proposed approach consists of four phases: preprocessing, key point generation, key point selection and angular encoding, and classification. The main idea is to generate key points using MediaPipe face mesh algorithm, which is based on real-time deep learning. In addition, the generated key points are encoded using a sequence of carefully designed mesh generator and angular encoding modules. Furthermore, feature decomposition is performed using Principal Component Analysis (PCA). This phase is deployed to enhance the accuracy of emotion detection. Finally, the decomposed features are enrolled into a Machine Learning (ML) technique that depends on a Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Na\u00efve Bayes (NB), Logistic Regression (LR), or Random Forest (RF) classifier. Moreover, we deploy a Multilayer Perceptron (MLP) as an efficient deep neural network technique. The presented techniques are evaluated on different datasets with different evaluation metrics. The simulation results reveal that they achieve a superior performance with a human emotion detection accuracy of 97%, which ensures superiority among the efforts in this field.<\/jats:p>","DOI":"10.1155\/2022\/8032673","type":"journal-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T20:05:09Z","timestamp":1643832309000},"page":"1-16","source":"Crossref","is-referenced-by-count":92,"title":["Deploying Machine Learning Techniques for Human Emotion Detection"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7242-5281","authenticated-orcid":true,"given":"Ali I.","family":"Siam","sequence":"first","affiliation":[{"name":"Department of Embedded Network Systems Technology, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7322-1857","authenticated-orcid":true,"given":"Naglaa F.","family":"Soliman","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia"}]},{"given":"Abeer D.","family":"Algarni","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia"}]},{"given":"Fathi E.","family":"Abd El-Samie","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 84428, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7651-8362","authenticated-orcid":true,"given":"Ahmed","family":"Sedik","sequence":"additional","affiliation":[{"name":"Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, Kafrelsheikh University, Kafr El-Sheikh, Egypt"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ridd.2014.10.015"},{"key":"2","first-page":"1626","article-title":"Eyemotion: classifying facial expressions in VR using eye-tracking cameras","author":"S. 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