Improvement of face recognition performance using a new hybrid subspace classifier
Yazarlar (1)
Doç. Dr. Serkan KESER Kırşehir Ahi Evran Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | Signal Image and Video Processing (Q3) | ||
| Dergi ISSN | 1863-1703 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2023 |
| Cilt / Sayı / Sayfa | 17 / 5 / 2511–2520 | DOI | 10.1007/s11760-022-02468-w |
| Makale Linki | http://dx.doi.org/10.1007/s11760-022-02468-w | ||
| Özet |
| Multiple classification systems play an important role in increasing recognition performance, especially when using heterogeneous classifiers that effectively improve performance. In this study, a new hybrid classifier was designed using heterogeneous Fisherface and discriminative common vector approach (DCVA) subspace recognition methods, which gave successful results in face recognition. While the classification process of DCVA is based on the common properties of signals belonging to the classes, the classification process of Fisherface is based on the different properties of signals. To create a hybrid classifier, called the Hybrid DCVA-Fisherface, the classifiers' decision rules were combined using the Minimum Proportional Score Algorithm and Recognition Update Algorithm. In addition to the proposed subspace classifiers, convolutional neural networks, Transform learning-Alexnet, Alexnet + SVM, and … |
| Anahtar Kelimeler |
| Alexnet | CNN | DCVA | Fisherface | HDF | Multiple classification systems |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 3 |
| Scopus | 2 |
| Web of Science | 2 |