抄録
Identifying subtle human facial expressions is important in human interactions such as human-robot communications. We propose a method to classify two types of facial expression, neutral (expressionless) and the subtle expression of happiness, using an image acquired with a camera. In the proposed method, facial shape features such as wrinkle-angle or unevenness of face surface are extracted via Gabor filters in multiple ROIs on a human face. High angle resolution Gabor filters are used to detect subtle facial expressions and the positions of the detection window and Gabor filter parameters are optimized by a learning process using AdaBoost. Changes to facial expression are estimated by comparing an input image to a learned database with the features. Experimental results showed that the proposed system has a 0.84 precision rate and a 0.91 recall rate in the case of detecting subtle changes.
