| Abstract: | Facial expressions are a fundamental component of human communication. Recognizing
emotions conveyed through facial expressions helps us understand others' feelings, intentions,
and social cues, facilitating effective interaction and empathy. This paper uses the FER 2013
dataset to provide an extensive analysis of facial emotion recognition. This study's primary
objective was to select a suitable model for face emotion detection using transfer learning
techniques. The evaluation process focuses on assessing the accuracy of the models employed.
Specifically, to gauge whether interpolation yields improved outcomes, the researchers plan to
conduct an experimental analysis of the interpolation technique's effectiveness in upscaling
lower-resolution images. By systematically analyzing the impact of interpolation on image
quality and model performance, the study aims to provide empirical evidence regarding the
efficacy of this technique in enhancing the accuracy of the models employed in image processing
tasks specaly for face emotion detection. By systematically analyzing the impact of interpolation
on both image quality and model performance, this study seeks to offer empirical evidence
concerning the effectiveness of this technique in improving the accuracy of models utilized in
image processing tasks, particularly for facial emotion detection.
In order to classify seven distinct emotions this study tested with three alternative
convolutional neural network architectures: VGG16, Resnet50, and Inception V3 the accuracy
measurement like precision, recall and f-1 score metrics were used to illustrate the model's
performance and results using interpolation to a 48x48 size, this study could obtain a maximum
of 23 percent recall for all models examined. This study offers valuable insights into the efficacy
of various pre-trained CNN architectures and interpolation methods in the domain of facial
emotion detection. By assessing these models, it not only informs the selection of suitable
architectures and interpolation sizes for emotion detection tasks but also serves as a catalyst for
further research in this field. The findings not only guide practitioners in choosing optimal models
for their applications but also inspire additional investigations aimed at refining and advancing
the techniques used in facial emotion detection. |
