Finding Emotions in Faces: A Meta-Classifier
Not on the map. Why?
Abstract (arXiv)
Machine learning has been used to recognize emotions in faces, typically by looking for 8 different emotional states (neutral, happy, sad, surprise, fear, disgust, anger and contempt). We consider two approaches: feature recognition based on facial landmarks and deep learning on all pixels; each produced 58% overall accuracy. However, they produced different results on different images and thus we propose a new meta-classifier combining these approaches. It produces far better results with 77% accuracy
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 4 of its 15 references that are in the map. First posted on arXiv 20 Aug 2022.
Builds on
4 of 15 references.
References · 4
- 2016Wide & Deep Learning for Recommender Systemscited by 745
- 2018xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemscited by 277
- 2018Face Alignment in Full Pose Range: A 3D Total Solutioncited by 147
- 2020BReG-NeXt: Facial Affect Computing Using Adaptive Residual Networks With Bounded Gradientcited by 13
Built on by
Not cited yet.