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.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".