Optimization methods for face recognition algorithmes
The paper examines the main drawbacks of modern face recognition algorithms: low processing speed, high sensitivity to image quality and face positioning. A division into three approaches to face recognition algorithms optimization is proposed: optimization of feature weights, algorithm hyperparamet...
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| Datum: | 2025 |
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| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | Ukrainian |
| Veröffentlicht: |
PROBLEMS IN PROGRAMMING
2025
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| Online Zugang: | https://pp.isofts.kiev.ua/index.php/ojs1/article/view/766 |
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| Назва журналу: | Problems in programming |
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Problems in programming| Zusammenfassung: | The paper examines the main drawbacks of modern face recognition algorithms: low processing speed, high sensitivity to image quality and face positioning. A division into three approaches to face recognition algorithms optimization is proposed: optimization of feature weights, algorithm hyperparameters, and constructing an optimal distributed system architecture. Examples of the application of Particle Swarm Optimization, Cuckoo Search, Simulated Annealing, and genetic algorithms to overcome the mentioned limitations in existing algorithms are provided. The study demonstrates the advantages and disadvantages of these optimization methods and identifies promising directions for further research in face identification methods optimization using genetic algorithms.Prombles in programming 2025; 1: 74-81 |
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