DIGITAL VIDEO IMAGES IN FORENSIC IDENTIFICATION
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: the article discusses the modern possibilities and prospects for the development of technologies for automatic identification of a person by his video images. Some legal and technical aspects of the implementation of this technology are analyzed. Attention is drawn to the factors affecting the completeness and reliability of the display of human features captured on digital video images. An important place is occupied by the study of problems in extracting information from video recording tools, ways to eliminate them. The possibilities of using the information obtained from the means of video recording in the practice of disclosure and investigation of crimes are reflected. The practice of implementing hardware and software complexes in the Russian Federation and the Perm Region is investigated. The article describes the development of algorithms for machine image recognition that allow identification in automatic mode with a high level of accuracy and speed, searching through information arrays of digital photographs. The development of modern technologies makes it possible to use in practice an algorithm that allows recognizing not only a face, but also its emotional state, up to complex, composite emotions. This is of particular relevance in the prevention of terrorist acts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it