@article{m_facial_2021,
	title = {Facial geometric feature extraction based emotional expression classification using machine learning algorithms},
	volume = {16},
	issn = {1932-6203},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247131},
	doi = {10.1371/journal.pone.0247131},
	abstract = {Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17\% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.},
	language = {en},
	number = {2},
	urldate = {2021-05-26},
	journal = {PLOS ONE},
	author = {M, Murugappan and A, Mutawa},
	month = feb,
	year = {2021},
	note = {Publisher: Public Library of Science},
	keywords = {Emotions, Face, Face recognition, Facial expressions, Fear, Happiness, Machine learning, Machine learning algorithms},
	pages = {e0247131},
}

{"_id":"rHFMwLu5k5Wrf4Evy","bibbaseid":"m-a-facialgeometricfeatureextractionbasedemotionalexpressionclassificationusingmachinelearningalgorithms-2021","author_short":["M, M.","A, M."],"bibdata":{"bibtype":"article","type":"article","title":"Facial geometric feature extraction based emotional expression classification using machine learning algorithms","volume":"16","issn":"1932-6203","url":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247131","doi":"10.1371/journal.pone.0247131","abstract":"Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.","language":"en","number":"2","urldate":"2021-05-26","journal":"PLOS ONE","author":[{"propositions":[],"lastnames":["M"],"firstnames":["Murugappan"],"suffixes":[]},{"propositions":[],"lastnames":["A"],"firstnames":["Mutawa"],"suffixes":[]}],"month":"February","year":"2021","note":"Publisher: Public Library of Science","keywords":"Emotions, Face, Face recognition, Facial expressions, Fear, Happiness, Machine learning, Machine learning algorithms","pages":"e0247131","bibtex":"@article{m_facial_2021,\n\ttitle = {Facial geometric feature extraction based emotional expression classification using machine learning algorithms},\n\tvolume = {16},\n\tissn = {1932-6203},\n\turl = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247131},\n\tdoi = {10.1371/journal.pone.0247131},\n\tabstract = {Emotion plays a significant role in interpersonal communication and also improving social life. In recent years, facial emotion recognition is highly adopted in developing human-computer interfaces (HCI) and humanoid robots. In this work, a triangulation method for extracting a novel set of geometric features is proposed to classify six emotional expressions (sadness, anger, fear, surprise, disgust, and happiness) using computer-generated markers. The subject’s face is recognized by using Haar-like features. A mathematical model has been applied to positions of eight virtual markers in a defined location on the subject’s face in an automated way. Five triangles are formed by manipulating eight markers’ positions as an edge of each triangle. Later, these eight markers are uninterruptedly tracked by Lucas- Kanade optical flow algorithm while subjects’ articulating facial expressions. The movement of the markers during facial expression directly changes the property of each triangle. The area of the triangle (AoT), Inscribed circle circumference (ICC), and the Inscribed circle area of a triangle (ICAT) are extracted as features to classify the facial emotions. These features are used to distinguish six different facial emotions using various types of machine learning algorithms. The inscribed circle area of the triangle (ICAT) feature gives a maximum mean classification rate of 98.17\\% using a Random Forest (RF) classifier compared to other features and classifiers in distinguishing emotional expressions.},\n\tlanguage = {en},\n\tnumber = {2},\n\turldate = {2021-05-26},\n\tjournal = {PLOS ONE},\n\tauthor = {M, Murugappan and A, Mutawa},\n\tmonth = feb,\n\tyear = {2021},\n\tnote = {Publisher: Public Library of Science},\n\tkeywords = {Emotions, Face, Face recognition, Facial expressions, Fear, Happiness, Machine learning, Machine learning algorithms},\n\tpages = {e0247131},\n}\n\n","author_short":["M, M.","A, M."],"key":"m_facial_2021","id":"m_facial_2021","bibbaseid":"m-a-facialgeometricfeatureextractionbasedemotionalexpressionclassificationusingmachinelearningalgorithms-2021","role":"author","urls":{"Paper":"https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247131"},"keyword":["Emotions","Face","Face recognition","Facial expressions","Fear","Happiness","Machine learning","Machine learning algorithms"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/users/6538716/collections/89FUBVDI/items?key=eWyANMLLCecfw2Uc56WwG3XK&format=bibtex&limit=100","dataSources":["rwKSE3KEuiYDFrF5T","Wo3KRwzN9T9tb8ucf"],"keywords":["emotions","face","face recognition","facial expressions","fear","happiness","machine learning","machine learning algorithms"],"search_terms":["facial","geometric","feature","extraction","based","emotional","expression","classification","using","machine","learning","algorithms","m","a"],"title":"Facial geometric feature extraction based emotional expression classification using machine learning algorithms","year":2021}