SST-HID: Scale-aware and shift-invariant teeth feature matching for human identification using OBB and SSIM

Teeth-based human identification is particularly valuable in forensic investigations due to the durability of dental structures under challenging conditions. This study investigates the use of Oriented Bounding Boxes (OBBs) for the geometric representation of tooth structures. Unlike Horizontal Bounding Boxes (HBBs), OBBs accurately localize rotated or irregularly shaped objects. OBBs align with the natural orientation of the teeth, providing a more accurate geometric representation of the individual dental structures, since each tooth has a unique orientation. Unlike existing works, we propose a matching approach that uses the Structural Similarity Index Measure (SSIM) for scale correction and Rotated Intersection over Union (RIoU) for feature matching, ensuring scale-aware identification. Furthermore, we account for the variability in the interspace between the upper and lower jaw at different time periods, we apply a statistical mean to determine the shift vector, thereby making our identification method shift invariant. The experimental results confirm the effectiveness of our strategy, with the highest precision and recall observed at IoU thresholds ranging from 0.5 to 0.9. These findings highlight the potential of OBB-based detection in forensic and biometric applications. The proposed method can be applied to disaster victim identification, criminal investigations, and scenarios where traditional biometric data (e.g., fingerprints or facial recognition) are unavailable.