AI/ML

Limitations and Biases in Facial Landmark Detection — An Empirical Study on Older Adults with Dementia. (arXiv:1905.07446v1 [cs.CV])

Accurate facial expression analysis is an essential step in various clinical
applications that involve physical and mental health assessments of older
adults (e.g. diagnosis of pain or depression). Although remarkable progress has
been achieved toward developing robust facial landmark detection methods,
state-of-the-art methods still face many challenges when encountering
uncontrolled environments, different ranges of facial expressions, and
different demographics of the population. A recent study has revealed that the
health status of individuals can also affect the performance of facial landmark
detection methods on front views of faces. In this work, we investigate this
matter in a much greater context using seven facial landmark detection methods.
We perform our evaluation not only on frontal faces but also on profile faces
and in various regions of the face. Our results shed light on limitations of
the existing methods and challenges of applying these methods in clinical
settings by indicating: 1) a significant difference between the performance of
state-of-the-art when tested on the profile or frontal faces of individuals
with vs. without dementia; 2) insights on the existing bias for all regions of
the face; and 3) the presence of this bias despite re-training/fine-tuning with
various configurations of six datasets.

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