Novel hybrid optimization techniques for enhanced generalization and faster convergence in deep learning models: the nestyogi approach to facial biometrics
Yükleniyor...
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute (MDPI)
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In the rapidly evolving field of biometric authentication, deep learning has become a cornerstone technology for face detection and recognition tasks. However, traditional optimizers often struggle with challenges such as overfitting, slow convergence, and limited generalization across diverse datasets. To address these issues, this paper introduces NestYogi, a novel hybrid optimization algorithm that integrates the adaptive learning capabilities of the Yogi optimizer, anticipatory updates of Nesterov momentum, and the generalization power of stochastic weight averaging (SWA). This combination significantly improves both the convergence rate and overall accuracy of deep neural networks, even when trained from scratch. Extensive data augmentation techniques, including noise and blur, were employed to ensure the robustness of the model across diverse conditions. NestYogi was rigorously evaluated on two benchmark datasets Labeled Faces in the Wild (LFW) and YouTube Faces (YTF), demonstrating superior performance with a detection accuracy reaching 98% and a recognition accuracy up to 98.6%, outperforming existing optimization strategies. These results emphasize NestYogi’s potential to overcome critical challenges in face detection and recognition, offering a robust solution for achieving state-of-the-art performance in real-world applications.
Açıklama
Anahtar Kelimeler
Biometric Authentication, Face Detection, Face Recognition, Nesterov Momentum, Stochastic Weight Averaging (SWA), Triplet Loss, Yogi Algorithm
Kaynak
Mathematics
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
12
Sayı
18
Künye
Altaher, R., Koyuncu, H. (2024). Novel hybrid optimization techniques for enhanced generalization and faster convergence in deep learning models: the nestyogi approach to facial biometrics. Mathematics, 12(18). 10.3390/math12182919
