Face Liveness Detection using Feature Fusion Using Block Truncation Code Technique
Bibliographic record
Abstract
Nowadays the system which holds private and confidential data are being protected using biometric password such as finger recognition, voice recognition, eyries and face recognition. Face recognition match the current user face with faces present in the database of that security system and it has one major drawback that it never works better if it doesn’t have liveness detection. These face recognition system can be spoofed using various traits. Spoofing is accessing a system software or data by harming the biometric recognition security system. These biometric systems can be easily attacked by spoofs like peoples face images, masks and videos which are easily available from social media. The proposed work mainly focused on detecting the spoofing attack by training the system. Spoofing methods like photo, mask or video image can be easily identified by this method. This paper proposed a fusion technique where different features of an image are combining together so that it can give best accuracy in terms of distinguish between spoof and live face. Also a comparative study is done of machine learning classifiers to find out which classifiers gives best accuracy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".