Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16
Gede Putra Kusuma, Jonathan Jonathan, Andreas Pangestu Lim
Abstract
Geometry classification model which was pretrained on ImageNet dataset and fine-tuned for emotion classification. The classification is performed on the publicly available FER-2013 dataset of over 35,000 face images with in-the-wild setting for 7 distinct emotions with the provided 80% training, 10% validation, and 10% testing data distributions. The proposed approach outperforms most standalone-based model results with 69.40% accuracy.
Topics & Concepts
Face (sociological concept)Facial recognition systemComputer visionArtificial intelligenceComputer sciencePsychologyPattern recognition (psychology)LinguisticsPhilosophyFace and Expression RecognitionFace recognition and analysisEmotion and Mood Recognition