Abstract
Deep face recognition is a significant area of biometric authentication that addresses challenges such as low resolution, varying facial expressions, and inconsistent lighting. This paper presents a robust deep-learning approach to tackle these challenges. The study aims to employ multi-criteria decision-making techniques and verify the influence of individual and group expert opinions in decision-making. However, balancing criteria such as accuracy, sensitivity, specificity, precision, and recall remain challenging across different models. To fill this gap, the study utilized a decision-support framework that included the fuzzy analytical hierarchical process to set criteria weights based on expert input and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to select the optimal model. JAFEE3 in the LDN+DB model emerged as the best option, while LWF4 in the LDN+AB model proved to be the least effective. These results are valuable for researchers engaged in image processing, machine learning, and decision-making techniques.
Recommended Citation
Alazzawi, Abdulbasit; Yas, Qahtan M.; and Albayati, Burhan
(2025)
"A Group Decision-Making for Selecting Multi-Deep Face Recognition Models,"
Iraqi Journal for Computer Science and Mathematics: Vol. 6:
Iss.
2, Article 21.
DOI: https://doi.org/10.52866/2788-7421.1262
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol6/iss2/21
