Segmentation and root localization for analysis of dental radiographs

During lifetime, teeth are exposed to many effects like abrasion, loss and dental treatments. These effects along with natural shapes of teeth form a unique dental frame which contains useful attributes to be used for human identification. Today, there exist automated dental identification systems which are used by forensics of law departments. These systems need to extract dental structures like teeth or roots prior to further analysis. So far, in several studies, much effort has been paid for this task. However, there still exist core problems like automated detection of region of interest (ROI) and segmentation in panoramic dental radiographs with missing teeth. This study aims to present a tool that can be employed to overcome these issues. Unlike previous works, the proposed methodology takes advantage of discrete wavelet transform for more accurate localization of ROI and polynomial regression to form a smooth border, separating upper and lower jaws even in case of absent teeth. Results indicate that the proposed approach can be effectively used for teeth segmentation and root apex detection

Face recognition based on Kinect

In this paper, we present a new algorithm that utilizes low-quality red, green, blue and depth (RGB-D) data from the Kinect sensor for face recognition under challenging conditions. This algorithm extracts multiple features and fuses them at the feature level. A Finer Feature Fusion technique is developed that removes redundant information and retains only the meaningful features for possible maximum class separability. We also introduce a new 3D face database acquired with the Kinect sensor which has released to the research community. This database contains over 5,000 facial images (RGB-D) of 52 individuals under varying pose, expression, illumination and occlusions. Under the first three variations and using only the noisy depth data, the proposed algorithm can achieve 72.5 % recognition rate which is significantly higher than the 41.9 % achieved by the baseline LDA method. Combined with the texture information, 91.3 % recognition rate has achieved under illumination, pose and expression variations. These results suggest the feasibility of low-cost 3D sensors for real-time face recognition