  Passive biometric identification enables wildlife monitoring with minimal
disturbance. Using a motion-activated camera placed at an elevated position and
facing downwards, we collected images of sea turtle carapace, each belonging to
one of sixteen Chelonia mydas juveniles. We then learned co-variant and robust
image descriptors from these images, enabling indexing and retrieval. In this
work, we presented several classification results of sea turtle carapaces using
the learned image descriptors. We found that a template-based descriptor, i.e.,
Histogram of Oriented Gradients (HOG) performed exceedingly better during
classification than keypoint-based descriptors. For our dataset, a
high-dimensional descriptor is a must due to the minimal gradient and color
information inside the carapace images. Using HOG, we obtained an average
classification accuracy of 65%.
