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A Comparative Study on the Effects of Pooling on FER CNN Models

by Muskan Agrawal, Padmavati Shrivastasva, Rahul R. Pillai, Chirag Budhwani, Shivam Khare
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 37
Year of Publication: 2022
Authors: Muskan Agrawal, Padmavati Shrivastasva, Rahul R. Pillai, Chirag Budhwani, Shivam Khare
10.5120/ijca2022922463

Muskan Agrawal, Padmavati Shrivastasva, Rahul R. Pillai, Chirag Budhwani, Shivam Khare . A Comparative Study on the Effects of Pooling on FER CNN Models. International Journal of Computer Applications. 184, 37 ( Nov 2022), 7-14. DOI=10.5120/ijca2022922463

@article{ 10.5120/ijca2022922463,
author = { Muskan Agrawal, Padmavati Shrivastasva, Rahul R. Pillai, Chirag Budhwani, Shivam Khare },
title = { A Comparative Study on the Effects of Pooling on FER CNN Models },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2022 },
volume = { 184 },
number = { 37 },
month = { Nov },
year = { 2022 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number37/32554-2022922463/ },
doi = { 10.5120/ijca2022922463 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:23:21.842586+05:30
%A Muskan Agrawal
%A Padmavati Shrivastasva
%A Rahul R. Pillai
%A Chirag Budhwani
%A Shivam Khare
%T A Comparative Study on the Effects of Pooling on FER CNN Models
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 37
%P 7-14
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emotion recognition has attracted much attention in Artificial Intelligence in order to make machines understand emotional sentiments, with many industries trying hard to incorporate emotion recognition technologies into their products. The easiest way to detect a person’s emotion is recognizing their facial expressions. In this work, the researchers tend to use FER as a problem and use the Deep Convolutional Neural Network (DCNN), which extracts the features automatically and therefore surpasses and outperforms the limitations of traditional machine learning. This work provides a comparative study of various existing pre-trained model architectures. MobileNetV2, MobileNetV3_Small, NASNetMobile, ResNet50, ResNet50V2, ResNet152V2, DenseNet169 and DenseNet201 with modification in their pooling layer to achieve high accuracy and have the potential for implementation in embedded systems. In this project, various deep learning pre-trained models were trained, tested, and compared on a modified subset of the FER 2013 Dataset for Face Emotion Recognition under all the conditions of pooling, i.e., None, Min, Avg, and Max. FER 2013 being one of the most challenging dataset, and due to limited run-time cost available, the MobileNetV2 model gave the highest testing accuracy of 83.64% with a training accuracy of 97.87% on average pooling. The models were compared on the following evaluation metrics: Accuracy, Loss, Precision, Recall and F1-score. For a practical approach, they integrate the model into a mobile application so that models can be run on devices in real time.

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Index Terms

Computer Science
Information Sciences

Keywords

Transfer Learning Pooling Face Emotion Recognition

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