An accurate active shape model for facial feature extraction
The active shape model (ASM) has been used successfully to extract the facial features of a face image under frontal view. However, its performance degrades when the face concerned is under perspective variations. In this paper, a modified shape model is proposed which can adapt to face images under different orientations. To make the model represent a face more flexibly, the representations of the important facial features, i.e. the eyes, nose and mouth, and the face contour are separated. An energy function is defined that links up these two representations of a human face. In order to represent a face image under different poses, three models are employed to represent the important facial features: the left-viewed, right-viewed, and frontal-viewed models. The genetic algorithm (GA) is applied to search for the best representation of face images. Experimental results demonstrate that our proposed method can achieve a better performance in representing face images under different perspective variations and facial expressions than the conventional ASM can.
active shape model facial feature extraction genetic algorithm