A novel fuzzy multimodal information fusion technology for human biometric traits identification
Bibliographic record
Abstract
In recent years, biometric based security systems achieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfill users' expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric systems with multiple physiological, behavioral, and soft biometric information are becoming more popular due to increased recognition accuracy. In order to take full advantage of the multimodal approaches, one of the main issues is to implement the fusion mechanism for different biometric information. In this research, we utilize the physiological attributes (face, ear and iris) along with soft biometric information (gender, ethnicity and eye color). A fuzzy fusion mechanism for robust and reliable multimodal biometric based security systems is developed. The proposed fuzzy fusion scheme adopts rank, match score and soft biometrics information as the input and produces final identification decision via a fuzzy rule-based inference system. The experimental results show that the fuzzy fusion method can provide us faster, higher and more reliable recognition performance than conventional unimodal methods. The system can be effectively used at any security critical applications.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".