Attendance System Based on Face Recognition Dependent on Deep Intelligent Techniques
Bibliographic record
Abstract
Face recognition technique has been one of the most important and intriguing fields of detection and observation recently.This is due to the increasing need for real-time, automatic recognition, and surveillance systems, as well as the growing interest in the human visual system's role in face recognition and the design of human-computer interfaces.The most modern methods used for this purpose are dependent on the neural network, especially the deep neural network.Typically, the conventional process of any face recognition system mainly consists of three stages: face detection, feature extraction, and face recognition.An attendance system based on detecting and recognizing the faces of employees or visitors at the University of Mosul in Iraq is presented, the proposed method recognizes the detected faces whether they belong to the university employees or not.The main contribution of this paper is combining intelligent techniques in an important application.By using this method, the faces of both employees and visitors can be detected and recognized immediately in each image.The Viola-Jones method is used for the face detection stage, while the second and third stages are combined due to the use of a deep learning approach.Convolutional Neural Networks (CNNs) as deep intelligent techniques are employed to extract features, followed by training the system with provided samples.This work gets its strength from the deep learning that extracts features using multiple layers in a convolutional manner.A robust image recognition mechanism is utilized to achieve high accuracy in the results, reaching a success rate of 96% across various image samples and scenarios.The proposed model of face recognition is mostly used in real-time applications, as they can be deployed in other universities or organizations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".