  Giant panda (panda) is a highly endangered animal. Significant efforts and
resources have been put on panda conservation. To measure effectiveness of
conservation schemes, estimating its population size in wild is an important
task. The current population estimation approaches, including
capture-recapture, human visual identification and collection of DNA from hair
or feces, are invasive, subjective, costly or even dangerous to the workers who
perform these tasks in wild. Cameras have been widely installed in the regions
where pandas live. It opens a new possibility for non-invasive image based
panda recognition. Panda face recognition is naturally a small dataset problem,
because of the number of pandas in the world and the number of qualified images
captured by the cameras in each encounter. In this paper, a panda face
recognition algorithm, which includes alignment, large feature set extraction
and matching is proposed and evaluated on a dataset consisting of 163 images.
The experimental results are encouraging.
