A Novel Face Recognition Using Specific Values from Deep Neural Network-based Landmarks
Bibliographic record
Abstract
The detection of facial landmarks has been an ongoing research topic for the past decade as it is used in facial recognition, facial expression analysis, and security purposes. This paper proposes a new face recognition algorithm that uses deep learning-based facial landmark detection algorithms to extract key features from images. Using the landmarks obtained from the applied deep neural network, three different features are extracted using specific values and used for face recognition. The use of specific values (SV) for facial recognition is the novelty of this work. Three specific values namely the cosine distance, angles and areas are derived from the coordinates of the landmarks. The recognition rates of faces using the extracted landmarks and SVs as the proposed features are evaluated through several experiments. Of the proposed features, the area provides the best result. Also, to investigate the effect of increasing the number of landmark points on the proposed face recognition rate, the MediaPipe face mesh algorithm is utilized. With the same chosen SVs, the recognition rate results are discussed when recognition was carried out with the increased number of landmarks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".