An Active Shape Model Parametric Approach for Facial Landmark Detection

Facial landmark detection is crucial on subsequent tasks related to the face recognition, three-dimensional face reconstruction, and so on. In this work, we present an active shape model parametric approach for facial landmark detection. Concretely, we propose to encode the landmark locations by the active shape model (ASM) parameters. Then, we leverage the power of cascade regression to estimate the ASM parameters. Final landmark locations can be decoded by the ASM parameters. Through this parametric way of predicting the landmark locations in another domain, it is more compact and effective compared with existing similar work. Experiments are conducted to validate the effectiveness of our method.