Design And Implementation Of Facial Expression Recognition Using Convolutional Neural Networks For Improved Performance Analysis
Keywords:
FER, CNN, Deep Learning,, Image Processing, Artificial IntelligenceAbstract
The numerous uses of facial expression recognition (FER) in intelligent surveillance and healthcare monitoring have propelled it to the forefront of computer vision and AI research. Based on convolutional neural networks (CNNs), FER is applied in this research. Typical face expression datasets will be utilised, including those for joy, sadness, rage, fear, surprise, disgust, and neutral emotions. To improve the quality of images and make learning models more effective, image processing processes like rescaling, grey-scale conversion, normalising, smoothing, and augmentation have been applied. A convolutional neural network (CNN) can identify emotional expressions by analysing facial features for distinguishing features. The results demonstrate that, in comparison to conventional machine learning algorithms, a CNN-based FER performs exceptionally well. Any intelligent application can use this FER technology to identify human emotions.
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