S4HI: A Novel Learning-Based Human Identification Method From Behavioural Data
Bibliographic record
Abstract
Behavioral biometrics have recently gained attention as a complementary key enabler of security and authentication, owing to the recent success of different deep learning architectures. State-of-the-art works explored the spatial information of gait signals using temporal features with convolutional or recurrent neural networks, or through combinations of both. At the same time, the recent success of transformers led to their use for gait-based identity recognition. However, their efficient use is hindered by the structure of data, their features, and their long training times. Alternatively, in this paper, we propose a novel identity recognition solution that relies on structural state space models to handle the long range dependencies of the inertial measurement unit (IMU) data and leverages the strengths of both convolutional and recurrent architectures. This duality provides a robust mechanism for handling sequential data by utilizing global convolutional views during training and recurrent views during inference realizing fast training, while the recurrent structure ensures fast inference. Through experiments, we demonstrate the superiority of our method in terms of accuracy (best 95.6%), complexity (up to 7 times less complex), and training time (up to 50 times faster), compared to the Transformer and other benchmarks, and for two different IMU datasets.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".