Finding Emotions in Faces: A Meta-Classifier
This addresses emotion recognition in faces, an incremental improvement over existing methods.
The paper tackled emotion recognition from faces by combining feature recognition based on facial landmarks and deep learning on all pixels, which individually achieved 58% accuracy, and proposed a meta-classifier that improved accuracy to 77%.
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