Failure Detection for Facial Landmark Detectors
This addresses the issue of failure recovery in facial landmark detection for applications like gender estimation, though it is incremental as it builds on existing detectors.
The paper tackled the problem of inaccurate facial landmark detection by developing confidence models to detect failures in two recent detectors, correctly identifying over 40% of failures on benchmarks like AFLW and HELEN and reducing gender estimation error by 12% with minimal computational overhead.
Most face applications depend heavily on the accuracy of the face and facial landmarks detectors employed. Prediction of attributes such as gender, age, and identity usually completely fail when the faces are badly aligned due to inaccurate facial landmark detection. Despite the impressive recent advances in face and facial landmark detection, little study is on the recovery from and detection of failures or inaccurate predictions. In this work we study two top recent facial landmark detectors and devise confidence models for their outputs. We validate our failure detection approaches on standard benchmarks (AFLW, HELEN) and correctly identify more than 40% of the failures in the outputs of the landmark detectors. Moreover, with our failure detection we can achieve a 12% error reduction on a gender estimation application at the cost of a small increase in computation.