Image Registration-Based Approach to Ranking Dental X-Ray Images for Human Forensic Identification
Bibliographic record
Abstract
Dental features have been widely used for forensic identification purposes. However, the point-by-point comparison performed by a forensic odontologist in mass disaster situations or missing persons cases would be cumbersome and time-consuming due to the large number of records that would need to be compared. Consequently, a move towards computer-aided dental identification systems is necessary. In this work, we propose a computer-aided framework for matching of dental radiographs based on image registration. Given a postmortem (PM) radiograph with a marked region of interest (ROI), we searched the database of antemortem (AM) radiographs to retrieve a closest match. To express the degree of similarity/overlap between two radiographs, we used the weighted sum of squared differences (SSD) cost function. The method was tested on a database of 571 radiographs belonging to 41 distinct individuals. In 90% of the identification trials, our method ranked the correct match in the top 10%. In all trials, the correct match was among the top 22%. These experiments indicated that matching dental records using the SSD cost function is a viable method for human dental identification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".