We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Emotion Unveiled: A Deep Learning Odyssey in Facial Expression Analysis for Intelligent HCI.
- Authors
AlZu’bi, Shadi; Elbes, Mohammed; Mughaid, Ala
- Abstract
Examining facial expressions is a crucial aspect that garners attention due to its significance in divulging emotional states. This study delves into employing a robust deep learning method for automatically analyzing and identifying facial emotions in images. The chosen technique revolves around the convolutional neural network (CNN) algorithm. A dataset containing images of individuals, each exhibiting distinct facial expressions, was curated. The emotions in these images were categorized into seven groups (angry, disgust, fear, happy, neutral, sad, surprise) based on the depicted emotional states. The approach comprises four primary steps: preprocessing the input facial images, utilizing image adjustments and data augmentation, employing the Viola and Jones technique for face detection and landmark localization, creating a numerical feature vector from the registered image for feature extraction, and inputting the extracted features into the CNN for classification. The proposed CNN model underwent application to classify facial emotions within the image dataset. Additionally, a pretrained VGG-16 model was incorporated into the classification process for facial images. A comparative analysis between the proposed CNN approach and the pretrained VGG-16 model revealed that the latter outperformed the former in terms of accuracy rate and loss function values when determining individuals’ facial expressions.
- Subjects
CONVOLUTIONAL neural networks; FACIAL expression &; emotions (Psychology); COMPUTER vision; DATA augmentation; FACIAL expression
- Publication
International Journal of Advances in Soft Computing & Its Applications, 2024, Vol 16, Issue 2, p315
- ISSN
2710-1274
- Publication type
Article
- DOI
10.15849/IJASCA.240730.19