  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.
