Year: 2025
Type: article
Abstract: Building a classification model on images based on convolutional neural network concerns several factors. Different solutions were explored and presented, which include the implementation of neural architecture search to automate the selection of a best architecture taking into consideration a certain dataset. To provide relevant materials in the development of a tunable model and utilization of the available Keras Tuner hyper models, this study was conducted. FASS, the developed tunable model to classify emotions trained on CK+ dataset was able to generate a model with 93.75% accuracy on test data. The performance of FACE-FASS, the developed convolutional neural network classification model, was higher than the performance of the model built from using the HyperResNet or HyperXception prebuild applications trained on the same dataset. Despite the higher performance, further study is recommended for a possible increase in its accuracy, as well as its application on other image datasets.
Authors: Rex P. Flejoles, Joey S. Aviles
Institution: Angeles University Foundation
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4412724449