A Deep Learning Approach for Biometric Security in Video Surveillance System Using Gait
Bibliographic record
Abstract
Video surveillance systems and biometrics inclusion play a significant part in various applications like a criminal investigation, medical rehabilitation, virtual reality, etc. Human Gait is a popular biometric where the individual is differentiated by unique limb actions and special ground reaction force. The unique movement of limb actions is called gait, and a record of 2-D aggregate floor response force through one walking cycle is called Cumulative Foot Pressure Images (CFPI). Both gait and cumulative foot pressure images can be acquired simultaneously of the same person during walking under a surveillance system for human identification. Accurate gait recognition is highly impactful for most applications and a major challenge for researchers due to various external factors like different shoes, mood, clothes, injuries etc., affects the individual gait. The novel system addresses the accuracy issue and proposes two models using the Deep Convolution Neural Network (DCNN) architecture on a large standard database, CASIA-D, containing gait pose and CFPI images of the same person. First, the model of the DCNN is trained using unique Gait Energy Image (GEI) features which reduce the computational time compared to other types of feature sets. The second model is prepared using the CFPI features. Experimentation has been carried out to evaluate the performance of these models with different optimization methods and activation functions and has proven that the DCNN model is far superior in building an accurate gait recognition system on large standard 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".