Adaptive facial point detection and emotion recognition for a humanoid robot
We propose a robust landmark detector to deal with pose variation and occlusions. SVRs and NNs are respectively used to estimate intensities of 18 selected AUs. Fuzzy c-means clustering is employed to detect seven basic and compound emotions. Our unsupervised facial point detector outperforms other supervised models. The overall development is integrated with a modern humanoid robot platform.
facial point detection emotion recognition pose variation occlusion action unit human robot interaction