Prediction of Facial Emotions using Deep Learning and Machine Learning Techniques

Title

Prediction of Facial Emotions using Deep Learning and Machine Learning Techniques

Creator

Ajayraj, C.; Ms, Asha; Pk, Sathish

Description

Facial expression prediction has gained considerable attention in recent years, particularly because of its applications in human-computer interaction. This paper compares a wide range of deep learning and machine learning models for the prediction of face emotion using the CK+ dataset proposed by Cohn-Kanade. The dataset is characterized by seven classes of emotions represented by the labels, namely surprise, happiness, disgust, anger, sadness, fear, and contempt, on 784 training, 98 validation, and 99 testing images. To further improve model performance, preprocessing techniques were employed that enhanced data efficiency. To increase variability in the data and reduce overfitting, all images were scaled to a 48*48 pixel resolution, pixel values were scaled to be between 0 and 1 for uniformity and the following data augmentation techniques were implemented: 10-degree rotation, horizontal flip, 0.15 zoom. The five models that were tested were CNN, SVM, VGG16, InceptionV3 and VGG19. The results demonstrate the high accuracy achieved by the CNN model which showed an accuracy of 98.98%, 99% and 99% in training, validation and test respectively. The SVM classifier got an accuracy of 99%. Both InceptionV3 and VGG19 on the other hand achieved competitive testing accuracy values of 90.91% and 97.98% respectively, while VGG16 got tested and reached an accuracy of 85.86%. 2025 IEEE.

Source

2025 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

CK+Dataset; Deep Learning; Emotion Prediction; Machine Learning; Model Comparison; Pre-processing Techniques

Coverage

Ajayraj C., Christ University, Department of CSE, Bangalore, India; Ms A., Christ University, Department of CSE, Bangalore, India; Pk S., Christ University, Department of CSE, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833153755-5;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICDSAAI65575.2025.11011786

https://www.scopus.com/pages/publications/105007976191?origin=resultslist