Bibliographic record
Abstract
The decision of fingerprint identification depends on the fingerprint examiner's knowledge and experience. The fingerprint identification process is a recognition process which can be described as a process from perceptual cognition to rational cognition. During the process, one of the factors that impacts the quality of fingerprint identification is the capability of the fingerprint examiner. Fingerprint examiners select corresponding minutiae on fingermark in comparison phase and the capability can be measured by fingerprint examiners' stability of minutiae selection. Some research has demonstrated that stability of minutiae selection has influenced the quality of fingerprint identification conclusion, hence it is critical for conducting such fundamental research on stability of minutiae selection for Chinese fingerprint examiners. Our research is focused on analysis of stability of minutiae selection between analysis phase and comparison phase and can help us to understand: how fingerprint examiners understand the minutiae of fingermark in analysis phase; how to control fingerprint impacts fingerprint examiners' decision in minutiae selection in comparison phase; what is the relationship between stability of minutiae selection and fingerprint identification ability. In this study 106 fingerprint agencies around China were invited to take a proficiency test and finish four trials from the same source. The data were collected by web-based software and were analyzed by R statistical software. The results show that different analysts performed differently and fingerprint quality impacted the stability of minutiae selection. If fingerprint quality values were high, examiners reported highly stable minutiae selection, while they reported highly unstable minutiae selection if quality values were low, especially on the border of high quality and low quality area. Stability of minutiae selection can be effectively measured by I, which is defined as Minutiae Variability Index. This suggests that there is a need for developing a tool to assess the quality of fingermarks to predict the performance of fingerprint examiners during the fingerprint identification process. According to distribution of I, manager can effectively evaluate identification ability of agency or examiner and then take effective measurement to improve the stability of minutiae selection(such as document identification activity and add more verification stage) and make sure the quality of the fingerprint 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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".