A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms
Bibliographic record
Abstract
The recognition of hand gestures in cluttered or complex environments is a vital research area in the human-computer interaction and computer vision fields due to its various potential applications, such as hand action analysis, driver hand behaviour monitoring, virtual reality, pose estimation, human action recognition, and sign language recognition. In order to create more reliable and efficient algorithms in this research field, various approaches have been suggested in recent years. However, a robust system is still elusive. For this reason, a new deep learning-based architecture for classifying hand gestures is suggested in this study; it is based on a hybrid model. The study makes two main contributions to the literature. The first is the creation of a new database for hand gesture recognition. The second is a novel hybrid architecture that combines two widely used pre-trained network models in an optimised manner, using a genetic algorithm for hyperparameter optimization. The proposed method comprises five main phases, namely, data acquisition, pre-processing, the design of the deep neural network architecture, hyperparameter optimization, the training of the proposed deep neural network architecture. The proposed method was tested on three comprehensive datasets. The experimental results reveal that the suggested method can effectively classify hand gestures with a high accuracy rate and that it outperforms the state-of-the-art methods in the literature.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".