File Exchange

image thumbnail

Live Emotion Detection using CNN a Deep Learning Model

version 1.0.2 (380 KB) by Akhilesh Kumar
Deep learning is a type of supervised machine learning in which a model learns to perform classification tasks directly from data.

12 Downloads

Updated 11 May 2020

View License

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.

Cite As

Akhilesh Kumar (2020). 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 .

Comments and Ratings (4)

Akhilesh Kumar

If you get cuda error , it means there is issue with your GPU, try to run model again after restarting MATLAB, or during training use cpu.
In a trainingoptions provide this property and value 'ExecutionEnvironment','cpu'

Stephen Forczyk

Getting the following error
Error using trainNetwork (line 150)
An unexpected error occurred trying to retrieve CUDA device properties. The CUDA error was:
CUDA_ERROR_UNKNOWN
How do I fix this!

Kenta

good example to learn deep learning! thanks

Updates

1.0.2

Updated Code

1.0.1

Dependency Check

MATLAB Release Compatibility
Created with R2020a
Compatible with R2017b to any release
Platform Compatibility
Windows macOS Linux