Towards Automated Biometric Identification of Sea Turtles (Chelonia mydas)

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%.

A Modular Approach and Voting Scheme on 3D Face Recognition

In this paper, we carried out a modular approach human 3D face recognition across neutral and six basic facial expressions experiments. Initially, a face model is decomposed into several modules before the 3D facial points for each of the modules are extracted. Three sizes of modules are used in our experiments: 2-Module, 6-Module and 10-Module. We apply Support Vector Machines as the classifier to each of the modules. A Majority Voting Scheme (MVS) and Weighted Voting Scheme (WVS) are constructed to infer the emotion underlying a collection of modules. From the analysis, we conclude that 10-Module outperforms 2-Module and 6-Module. In addition, the modules with low amount feature vectors and only contain boundary feature vectors perform worst