Facial Emotion Recognition Focused on Descriptive Region Segmentation

Facial emotion recognition (FER) is useful in many different applications and could offer significant benefit as part of feedback systems to train children with Autism Spectrum Disorder (ASD) who struggle to recognize facial expressions and emotions. This project explores the potential of real time FER based on the use of local regions of interest combined with a machine learning approach. Histogram of Oriented Gradients (HOG) was implemented for feature extraction, along with 3 different classifiers, 2 based on k-Nearest Neighbor and 1 using Support Vector Machine (SVM) classification. Model performance was compared using accuracy of randomly selected validation sets after training on random training sets of the Oulu-CASIA database. Image classes were distributed evenly, and accuracies of up to 98.44% were observed with small variation depending on data distributions. The region selection methodology provided a compromise between accuracy and number of extracted features, and validated the hypothesis a focus on smaller informative regions performs just as well as the entire image.

Automated summary

The project examines real-time facial emotion recognition based on local regions of interest and machine learning, achieving high accuracy rates up to 98.44% with different data distributions. The region selection methodology strikes a balance between accuracy and number of extracted features, validating the hypothesis that focusing on smaller informative regions performs as well as the entire image.