Live Emotion Detection using CNN a Deep Learning Model
Updated 11 May 2020
Deep learning is a type of supervised machine learning in which a model learns to perform classification tasks directly from images, text, or sound.
Deep learning is usually implemented using a neural network.
The term “deep” refers to the number of layers in the network—the more layers, the deeper the network.
A convolutional neural network can have hundreds of layers and each layer learn to detect different features of an image.
Filters are applied to each training image at different resolutions and size, and the output of each convolved image is used as the input to the next layer.
The filters can start as very simple features, such as brightness and edges, and later on it goes deep to extract complex features.
Like other neural networks, a CNN is composed of an input layer, an output layer, and many hidden layers in between.
Akhilesh Kumar (2023). Live Emotion Detection using CNN a Deep Learning Model (https://www.mathworks.com/matlabcentral/fileexchange/75451-live-emotion-detection-using-cnn-a-deep-learning-model), MATLAB Central File Exchange. Retrieved .
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