Robust representation and recognition of facial emotions
Seyedehsamaneh Shojaeilangari · 2014
Facial Emotion detection under natural conditions is an interesting topic with a wide range of potential applications like human-computer interaction.Although there is significant research progress in this field, there are still challenges related to real-world unconstrained situations.One essential challenge is to find pose invariant spatio-temporal volumetric features to analyze the video sequence efficiently.Another important issue is how to deal with noisy and imperfect data recorded in uncontrolled environments such as illumination variations, partial occlusion, and head movements.The focus of this research is to develop a robust system for facial expression recognition as a dynamic event in natural situations.Two strategies have been proposed in this research to address the uncontrolled environments related challenges: Robust representation framework: we propose a novel spatio-temporal descriptor based on Optical Flow (OF) components which is very distinctive and also poseinvariant. Robust recognition framework: we explored the effectiveness of sparse representation obtained by supervised learning a set of basis (dictionary).Extreme Sparse Learning (ESL) is proposed to jointly learn a dictionary and a nonlinear classification model to robustly detect the facial expression in real-world natural situations.The proposed approach combines the discriminative power of the Extreme Learning Machine (ELM) with the reconstruction property of the sparse representation to deal with noisy signal and imperfect data recorded in natural settings.