Robust Facial Landmark Detection via Recurrent Attentive-Refinement Networks
published: Oct. 24, 2016, recorded: October 2016, views: 48
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
In this work, we introduce a novel Recurrent Attentive-Refinement (RAR) network for facial landmark detection under unconstrained conditions, suffering from challenges like facial occlusions and/or pose variations. RAR follows the pipeline of cascaded regressions that refines landmark locations progressively. However, instead of updating all the landmark locations together, RAR refines the landmark locations sequentially at each recurrent stage. In this way, more reliable landmark points are refined earlier and help to infer locations of other challenging landmarks that may stay with occlusions and/or extreme poses. RAR can thus effectively control detection errors from those challenging landmarks and improve overall performance even in presence of heavy occlusions and/or extreme conditions. To determine the sequence of landmarks, RAR employs an attentive-refinement mechanism. The attention LSTM (A-LSTM) and refinement LSTM (R-LSTM) models are introduced in RAR. At each recurrent stage, A-LSTM implicitly identifies a reliable landmark as the attention center. Following the sequence of attention centers, R-LSTM sequentially refines the landmarks near or correlated with the attention centers and provides ultimate detection results finally. To further enhance algorithmic robustness, instead of using mean shape for initialization, RAR adaptively determines the initialization by selecting from a pool of shape centers clustered from all training shapes. As an end-to-end trainable model, RAR demonstrates superior performance in detecting challenging landmarks in comprehensive experiments and it also establishes new state-of-the-arts on the 300-W, COFW and AFLW benchmark datasets.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !