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Survey Paper on Fine-Grained Facial Expression Recognition using Machine Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Gunjal Vaishnavi, Gavane Shraddha, Joshi Yogeshwari

Gunjal Vaishnavi, Gavane Shraddha and Joshi Yogeshwari. Survey Paper on Fine-Grained Facial Expression Recognition using Machine Learning. International Journal of Computer Applications 183(11):47-49, June 2021. BibTeX

	author = {Gunjal Vaishnavi and Gavane Shraddha and Joshi Yogeshwari},
	title = {Survey Paper on Fine-Grained Facial Expression Recognition using Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {11},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {47-49},
	numpages = {3},
	url = {},
	doi = {10.5120/ijca2021921427},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


A computer to monitor emotions that can assess fundamental speech of the human face. This research proposes a mood forecast based on emotions of the human face. The instrument to detect the human mood and to play an audio file with this effect that refers to human emotions. Next, the computer takes the human face as its input, so another move is taken. The face and eye are identified. This is done. The human face is then recognized by the technique of extraction of the attributes. In this way, a face picture feature recognizes the emotion of the individual. The lips, mouth and eyes and the eyebrow extract these signature marks. If the emotional face matches the emotional dataset exactly, the exact emotions of people can be defined to play the audio-file with the emotional details. Training on a limited number of faces would be recognized in different environmental circumstances. The proposed solution is quick, efficient and accurate. The machine plays an increasingly important part in the field of identification and detection.


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Face Detection, Feature Extraction, Face Emotion, Machine Learning.