Age-Dependent Palm Print Recognition Using Convolutional Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Biometric engineering is one of the most important and modern fields that affect human life directly.It can be considered as a new technology relatively, that is used for identity verification and/or the identification of persons depending on their physiological features, which include the morphological, biological, and characteristics of their behaviors.Many types of biometric recognitions are used depending on features of eyes, faces, hands (palm and/or fingerprints), voice, and many others.All the works before were focused on persons' detection only but nor on their ages.This feature (age) considered as one of the not solved problems in the field of detection.In this paper, the palm recognition model consisted of many steps.The first step related to palm detection.Other techniques used to remove noisy portion from extracted image.After preparing images for training, a deep neural network represented by convolutional neural network is selected.A new idea and method (mechanism) is used.Palm print features' recognition algorithm depending on Convolutional Neural Network (CNN) is presented for recognizing individuals (persons recognition in different ages' classes).Palm print technique is depended for different ages' classes.The dataset is selected firstly for many known persons with different ages, for each person many palm image items are trained and tested using deep learning techniques.As mentioned, the CNN method is used for the training purpose, which means the recognition must be done depending on the CNN deep learning algorithm.The FAR and GAR factors are used to measure the performances of the recognition.The given results shown that the selection of the palm instead of other features types makes the recognition easier.More than 96% of the results were accurate.Also, the used algorithm which included the CNN had competitive performance, the algorithm succeeded to separate between the features according to the persons' ages.The overall process is completed within 0.01×10 -6 second, which can be considered fast and suggested to be used in real time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it