Facial landmark detection and geometric feature-based emotion recognition

Facial expression related to machine intelligence is a popular research area in emotion science, pain assessment, human behaviour analysis, virtual reality, etc. This paper aims at exploring a contour-based shape analysis from the viewpoint of geometric characteristics towards facial expression recognition. Since the facial landmark detection accuracy dramatically affects the final classification, a simple contour detection algorithm is used for identifying facial landmarks accurately. Spatial local and relative geometric features extracted with the neutral face as the reference are projected to the lower-dimensional space using stepwise linear discriminant analysis. The proposed system is tested and validated using backpropagation-based artificial neural network on JAFFE and MMI dataset with an average accuracy of 95.53% and 94.98%, respectively. The proposed scheme's recognition accuracy has been compared with the state-of-art methods, and the results show significant improvement in the proposed model over others using geometric features alone.