Personnel identification in mine underground based on maximin discriminant projection
Bibliographic record
Abstract
Due to the complexity and constrained space in underground mine,the images of human face,iris,fingerprint and palmprint often become blurred,the recognition rates of the mine underground personnel identification based on these biological characters are not higher than that in the regular environment. Based on Warshall algorithm and maximin criterion,a method of gait recognition,named maximin discriminant projection( MMDP),was proposed. In MMDP,the label relationship of the data was quickly explored by the Warshall algorithm. The within-class and betweenclass scatter matrices were constructed by the label relationship. Compared with the traditional gait recognition methods,the proposed method makes full use of the local information and class information of the gait data,so that in lowdimensionality projecting space,the distance between any pairwise samples belonging to the same class was reduced,while the distance between any pairwise samples coming from different classes was enlarged. Compared with the classical subspace dimensional reduction algorithms,in the proposed method,it was not necessary to judge whether two samples belong to the same class or not when constructing the within-class and between-class scatter matrices,which can improve the performance of the proposed algorithm. A series of gait recognition experiments were conducted on the real gait databases. Experimental results verify the proposed method is effective and feasible for mine underground personnel identification by using gait.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".