Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

Irwandi Hipiny, Hamimah Ujir, Aazani Mujahid, Nurhartini Kamalia Yahya

Abstract


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


Keywords


content-based image retrieval; invariant feature descriptor; multimedia databases; template matching; visual animal biometrics.

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DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2018.12.3.4

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