Rank level fusion for kinect gait and face biometrie identification
Bibliographic record
Abstract
Multimodal biometrie systems play a significant role in ensuring an uneompromised access to secure resources and facilities. Their resilience to spoof attacks, increased accuracy and ability to handle noisy data, intra-class variability and inter-class similarities have led to multi-modal systems being accepted as an industry standard. This article presents a first multimodal biometric system that combines KINECT gait modality with KINECT face modality utilizing the rank level fusion. As both gait and face biometric identifiers are collected from the Kinect camera, this provides an inexpensive and a convenient way to extract biometric features, as opposed to a standard video camera. For the KINECT gait modality, a new approach is proposed based on the skeletal information, while previous methods used silhouette data which lacks discriminability. The gait cycle is calculated using three consecutive local minima computed for the distance between left and right ankles. The feature distance vectors are calculated for each person's gait cycle, which allows to extract the biometric features such as the mean and variance of the feature distance vector. For Kinect face recognition, a novel method based on HOG features has been developed. Advantages of using HOG features lie in their ability to extract pertinent information from the gradient intensity of the facial image. Then, K-nearest neighbors feature matching algorithm is applied to feature classification for both gait and face biometrics. Finally, the Borda count and logistic regression approaches are used in the rank level fusion. The method achieves an accuracy of 93.33% for Borda count and 96.67% for logistic regression methods on KINECT Gait and KINECT EUROCOM face 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) | 0.000 | 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".