Convolutional Neural Network Hyperparameters optimization for Facial Emotion Recognition

This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition. The optimal hyperparameters of the network were determined by generating and training models based on Random Search algorithm a...

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Bibliographic Details
Main Authors: Vulpe-Grigorasi, Adrian, Grigore, Ovidiu
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Convolutional neural networks
Electrical engineering
Emotion recognition
facial emotion recognition
FER2013
hyperparameter optimization
Limiting
Random Search
Training
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Summary:This paper presents a method of optimizing the hyperparameters of a convolutional neural network in order to increase accuracy in the context of facial emotion recognition. The optimal hyperparameters of the network were determined by generating and training models based on Random Search algorithm applied on a search space defined by discrete values of hyperparameters. The best model resulted was trained and evaluated using FER2013 database, obtaining an accuracy of 72.16%.
ISSN:2159-3604
DOI:10.1109/ATEE52255.2021.9425073