Integration of Face and Gait Recognition via Transfer Learning: A Multiscale Biometric Identification Approach
Bibliographic record
Abstract
The ubiquity of biometric identification systems and their applications is evident in today's world.Among various biometric features, face and gait are readily obtainable and thus hold significant value.Advances in computational vision and deep learning have paved the way for the integration of these biometric features at multiple scales.This study introduces a system for biometric recognition that synergises face and gait recognition through the lens of transfer learning.Feature extraction was accomplished using Inception_v3 and DenseNet201 algorithms, while classification was performed employing machine learning algorithms such as K-Nearest Neighbours (KNN) and Support Vector Classification (SVC).A unique dataset was constructed for this research, consisting of face and gait information extracted from video clips.The findings underscore the efficacy of integrating face and gait recognition, primarily through feature and score fusion, resulting in enhanced recognition accuracy.Specifically, the Inception_v3 algorithm was found to excel in feature extraction, and SVC was superior for classification purposes.The system achieved an accuracy of 98% when feature-level fusion was performed, and 97% accuracy was observed with score fusion using Decision Trees.The results highlight the potential of transfer learning in advancing multiscale biometric recognition systems.
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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 | 0.001 | 0.001 |
| 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".