Facial Expression Recognition of Autistic Children for Virtual Learning
DOI:
https://doi.org/10.63252/JCBECA/1.1.2024Keywords:
facial expression recognition, neural network, support vector machine, local binary patterns, principal component analysisAbstract
This study presents a facial expression recognition system tailored for autistic children. Initially, images of autistic children are obtained from publicly available datasets and pre-processed using a median filter to eliminate noise. The pre-processed images are then subjected to techniques such as Principal Component Analysis (PCA), Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP) to identify key characteristics. A hybrid Neural Network-Support Vector Machine (NN-SVM) model designed for face expression identification uses these characteristics as inputs. The proposed system aims to enhance virtual learning for autistic kids by increasing the precision and resilience of emotion recognition.
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