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In this repo, I implemented VGGNet, MobileNet and AlexNet and compared their performance on Emotion Detection Task using AffectNet dataset.

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cnn emotion-detection emotion-recognition alexnet alexnet-model cnn-classification cnn-model vggnet mobilenet affectnet affectnet-dataset

cnn-based-facial-affect-analysis-on-mobile-devices's Introduction

CNN-based-Facial-Affect-Analysis-on-Mobile-Devices

In this repo, we tried to implement the paper, "CNN-based Facial Affect Analysis on Mobile Devices". The paper is add to the repo and you can download it.

Dataset

Dataset is provided in this link:

https://drive.google.com/file/d/1MknXcvOW7FhQrtLWYJkti6MwvZBkwWgu/view

Preprocessing

Since data is not balanced so we used the data augmentation to balance the classes. In the the dataset we have 8 different classes: ["anger", "contempt", "disgust", "fear", "happy", "neutral", "sad", "suprise"] We rotated(20 degrees), did the translation with respect to X and Y axis to % 10 percent and flipped images with repect to X axis.

AlexNet Implementation

Architucture of the AlexNet is as below:

image

We trained our model to 50 iterations, and the loss and accuracy plot are as below :

image

VGGNet

VGGNet Architecture is as below:

image

We trained our model to 10 iterations, and the loss and accuracy plot are as below :

image

image

image

MobileNet

VGGNet Architecture is as below:

image We trained our model to 25 iterations, and the loss and accuracy plot are as below :

image

if you have any questions, reach out to me via email: [email protected]

@article{DBLP:journals/corr/abs-1807-08775, author = {Charlie Hewitt and Hatice Gunes}, title = {CNN-based Facial Affect Analysis on Mobile Devices}, journal = {CoRR}, volume = {abs/1807.08775}, year = {2018}, url = {http://arxiv.org/abs/1807.08775}, eprinttype = {arXiv}, eprint = {1807.08775}, timestamp = {Mon, 13 Aug 2018 16:47:27 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-1807-08775.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

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