Bibliographic record
Abstract
Digital recognition with lip images has become a key step of the interactive liveness detection for Chinese banking systems. However, the problem of the digital recognition is very challenging due to intra class variation of lip images, head pose variations, and uncontrolled illumination. This paper studies a deep learning architecture to model the appearance and the spatial-temporal information of lip texture. The lip texture in still image frames and the spatial-temporal relationship between these frames are jointly modeled by convolutional neural networks and long short-term memory. Two strategies are further exploited to find effective groups of ten digitals for training the deep models. As a result, more information can be utilized for accurate recognition based on lip texture analysis. Besides, two datasets of isolated digits in Chinese are established to simulate real-world liveness detection environments together with various attacks. Extensive experiments have been done to analyze the recognition accuracy of each digit and to provide some clues for determining appropriate digits for interactive liveness detection.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".