Algorithm of Case-based Facial Action Units Recognition
基于事例的面部动作单元识别算法

This paper proposes a case-based automatic facial action units recognition approach using interactive genetic algorithm, which embeds human compare ability into search process. To obtain AU codes of a new facial image, IGA is applied to retrieve a match instance to this new one from the case base based on users~ evaluation. Then the solution suggested by the matching case is used as the AU codes to the new facial images. The approach is easy and has the latent capacity to detect AU codes from facial images and image sequences insensitive to pose and occlusions, since feature extraction is not needed. The effectiveness of our approach is evaluated by 16 standard images and 10 un-standard images. A recognition rate of 77.5% is achieved on single AUs, and a similarity rate of 82.8% is obtained on AU combinations to standard images. A recognition rate of 82.8% is achieved on single AUs, and a similarity rate of 93.1% is obtained on AU combinations to un-standard images. The compare experiments with eigenface method also demonstrate the effectiveness of our approach.