Facial expressions are one important nonverbal communication cue, as they can provide feedback in conversations between people and also in human-robot interaction. This paper presents an evaluation of three standard pattern recognition techniques (active appearance models, gabor energy filters, and raw images) for facial feedback interpretation in terms of valence (success and failure) and compares the results to the human performance. The used database contains videos of people interacting with a robot by teaching the names of several objects to it. After teaching, the robot should term the objects correctly. The subjects reacted to its answer while showing spontaneous facial expressions, which were classified in this work. One main result is that an automatic classification of facial expressions in terms of valence using simple standard pattern recognition techniques is possible with an accuracy comparable to the average human classification rate, but with a high variance between different subjects, likewise to the human performance.
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