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This work presents a mathematical model for emotional state inference based solely on facial expressions. The presented model consists of two basic steps, which are responsible for signal processing and its integration, respectively. During the former, independent Kalman filters are employed for parallel processing of emotional facial expression related signals. The later step, integration, applies those filtered signals to a system where a massless particle slides along a surface representing the emotional states of interest and its transitions. The subject’s emotional state is inferred from the particle’s instantaneous position at each frame. A simulation-optimization heuristic based on simulated annealing is introduced as for fully automatic parameter tuning technique, which allows for easily coupling between the proposed model and different facial expression detection algorithms. The proposed model is validated against 51 multimodal emotional videos. The results are compared to human-based classification and a 92% agreement rate is observed.

Um modelo matemático para inferência computacional de estado emocional a partir de detectores de expressões faciais.