A Biometric System for Personal Identification Using Modular Neural Nets.(Dept.E)
Bibliographic record
Abstract
In this paper, a fast biometric system for face recognition is introduced. We combine both fast and cooperative modular neural nets (MNNs) to enhance the performance of the detection process Such approach is applied to identify frontal views of human faces automatically in cluttered scenes. In the detection phase. neural nets are used to test whether a window of 20x20 pixels contains a face or not The large number of examples required for face and nonface images makes the convergence process very difficult during the learning process. A simple design for cooperative modular neural nets is presented to solve this problem by dividing these data into three groups Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. For the recognition phase, feature measurements are made through Fourier descriptors which are insensitive to rotation, translation and scaling Such feature is modified to reduce the number of neurons in the hidden layer. From these features, wavelet coefficients are extracted which have been shown to provide advantages in terms of better representation for a given data to be compressed finally, the resulted vector is fed to a neural net for face classification. Simulation results for the proposed algorithm show a good performance.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".