Emerging EEG and kinect face fusion for biometrie identification
Bibliographic record
Abstract
Multimodal biometrie systems attract attention as security systems due to their ability to eliminate the limitations of unimodal systems, such as noisy data, intra-elass variability, inter-class similarities etc. This article presents for the first time a multimodal biometric system that combines emerging electroencephalograph (EEG) brain signal with the KINECT face modality by utilizing match score level fusion. For the EEG modality, we have considered motor imagery EEG brain signals and proposed a novel combination of time-frequency features extraction in various bands of EEG signal by utilizing the back propagation (BP) classification algorithm. For the Kinect face modality, a novel approach based on histogram oriented gradient (HOG) feature has been proposed. The major advantage of using the HOG feature is it allows to extract the prominent information from the gradient intensity of the facial image. The K-nearest neighbors (KNN) feature matching algorithm is then used for feature classification. Finally, the match score fusion technique has been applied to the outcomes of individual bands of the brain signal identification system and face recognition systems. The method achieves an accuracy of close to 100% for the fusion of EEG and KINECT EUROCOM face datasets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 0.001 |
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".