| Title | Emotion Recognition from 2D Facial Expressions |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Taha, Bilal, Hatzinakos, Dimitrios |
| Conference Name | 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) |
| Date Published | may |
| Keywords | 2d facial expressions, CNN model, Computational modeling, convolutional neural nets, convolutional neural network, Databases, Deep Learning, emotion recognition, Face, face recognition, facial expression recognition, facial expression recognition application, facial recognition, feature extraction, gray-level images, handcrafted features, Human Behavior, image classification, image texture, informative representations, learning (artificial intelligence), Metrics, overfitting problem, pubcrawl, Resiliency, shape features, span texture, Support vector machines, SVM classifier, Training |
| Abstract | This work proposes an approach to find and learn informative representations from 2 dimensional gray-level images for facial expression recognition application. The learned features are obtained from a designed convolutional neural network (CNN). The developed CNN enables us to learn features from the images in a highly efficient manner by cascading different layers together. The developed model is computationally efficient since it does not consist of a huge number of layers and at the same time it takes into consideration the overfitting problem. The outcomes from the developed CNN are compared to handcrafted features that span texture and shape features. The experiments conducted on the Bosphours database show that the developed CNN model outperforms the handcrafted features when coupled with a Support Vector Machines (SVM) classifier. |
| DOI | 10.1109/CCECE.2019.8861751 |
| Citation Key | taha_emotion_2019 |
Emotion Recognition from 2D Facial Expressions