Confidence assessment on eyelid and eyebrow expression recognition

In this paper, we address the recognition of subtle facial expressions by reasoning on the classification confidence. Psychological evidences have determined that eyelids and eyebrows are significant for the recognition of subtle facial expressions and the early perception of human emotions. This early perception results in a more complex problem, which requires a confidence assessment for any provided solution. Thus, traditional score-based classifiers (e.g. k-NN and NN) are not able to produce confident estimates. Instead, we first present five confidence estimators and a confidence classification assessment for Case-Based Reasoning (CBR). Second, we improve the expression retrieval from the database by learning the neighbourhood's dimensions for the expected classification confidences. Third, we reuse the previous classified expressions and the confidence assessment to improve the classification achieved by k-NN. Fourth, we improve the database for generalization with new subjects by learning thresholds to minimize misclassification with low confidence, maximize correct classifications with high confidence and re-arrange misclassification with high confidence. The proposed system represents an effective contribution for both subtle expression recognition and CBR methodology. It achieves an average recognition of 97% plusmn 1% with a confidence of 96% plusmn 2% for expressiveness between 20% and 100%.