Fingerprint recording with bolometric detectors
Bibliographic record
Abstract
Modified thermal sensors have been produced and characterized for fingerprint recording applications. The sensors are derived from the IR imaging technology developed at INO. The sensor array is made of 160x120 pixel VOx based micro thermistors that provide an image of a surface area of 8.3 x 6.2 mm2 with a resolution of 488 dpi. The sensors were reinforced to withstand the mechanical pressure of the finger and the electrical discharges from the human skin. It is shown that despite their low thermal insulation, the sensors provide an image of the fingerprint pattern with relatively high contrast and resolution. With the acquisition electronics of an IR imager, the temprature of the sensor must be controlled. Measurements of the thermistor temperature were performed in order to access the intrinsic properties of the fingerprint sensors. The NETD is on the order of 2 10-3°C when the pass band of the filter is 330 kHz. The temporal behavior of the thermistor temperature shows that 10 ms after the finger has been brought into contact, with the sensor, the temperature difference between thermistors in ridge and valley areas of the fingerprint DTr,v may reach 80 10-3°C, for an initial temperature difference between the finger and the sensor of 1°C. Once the sensor reaches a steady thermal state after a long time, the same difference decreases to 1.9 10-3°C. The required temperature difference DTr,v, estimated to be 4.8 10-3°C to achieve an adequate signal to noise ratio, is relatively easy to reach at short and at long time periods. A modification to the method of acquisition is proposed to cancel the effect of the thermal drift of the sensor and to eliminate the need for the sensor temperature stabilization with a TEC. With this method, the recording of the fingerprint pattern may be achieved in 50 ms after the finger has been brought into contact. This leads to interesting gains in space, time and power consumption. Finally, for applications where the finger must remain in contact with the sensor, the same method may be efficient to reduce the need for thermal control.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".