An Approach for Improving Automatic Mouth Emotion Recognition
This incremental work aims to support people with health disorders like stroke or autism by improving communication through emotion recognition.
The study tackled automated emotion recognition through mouth detection using Convolutional Neural Networks (CNN) and Haar Feature-based Classifiers, achieving fast execution and promising performance for real-time feedback in health applications.
The study proposes and tests a technique for automated emotion recognition through mouth detection via Convolutional Neural Networks (CNN), meant to be applied for supporting people with health disorders with communication skills issues (e.g. muscle wasting, stroke, autism, or, more simply, pain) in order to recognize emotions and generate real-time feedback, or data feeding supporting systems. The software system starts the computation identifying if a face is present on the acquired image, then it looks for the mouth location and extracts the corresponding features. Both tasks are carried out using Haar Feature-based Classifiers, which guarantee fast execution and promising performance. If our previous works focused on visual micro-expressions for personalized training on a single user, this strategy aims to train the system also on generalized faces data sets.