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Artificial Intelligence has many significant fields of work and expression/ emotion detection is one of them. To detect a facial expression, the system should analyze various variability of human faces like color, posture, expression, orientation, lighting, etc. Detecting facial features is a prerequisite to facial emotion recognition. This is achieved by observing the parts of the face, like eyes, lips movement, etc. These are then classified and compared to trained sets of data. In this research, a human facial expression recognition system will be modeled using the eigenface approach. The proposed method will use the HAAR Cascade classifier to detect the face in an image. After that anticipating the test picture upon the eigenspace and computing the Euclidean separation between the test picture and meaning of the eigenfaces. fer2013 dataset will be used for training purposes. The grayscale image of the face is used by the system to classify five basic emotions such as surprise, disgust, neutral, anger, and happiness.
Keywords:
HAAR Cascade classifier, fer2013 dataset, feature extraction, CNN, Xception architecture, ReLU activation function
Cite Article:
"FACIAL EMOTION DETECTION AND RECOGNITION ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 11, page no.44 - 52, November-2022, Available :http://www.ijrti.org/papers/IJRTI2211008.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator