An investigational FW-MPM-LSTM approach for face recognition using defective data
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Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Facial recognition systems are listed as a biometric system, because they are directly related
to the facial features and characteristics. They are also based on the principles of image
processing, machine vision and sometimes machine learning. Face recognition systems may
consider imperfect information from images. In this case, it is essential to provide a series
of image reconstruction mechanisms for matching faces. In this paper a robust method was
implemented and tested on the face recognition dataset based on the image’s segmentation
techniques. The proposed approach is that in the pre-processing phase, image should be
enhanced. The image segmentation and reconstruction step is then followed by extracting
the best facial features using features such as lips, eyes, cheeks and face area. This operation
is based on fractal model and wavelet transform. Next, to train and test the system, the LSTM
neural network is optimized using a method called Moore Penrose Matrix which named the
MPM-LSTM. The results represent the proposed approach have better performance in
comparison to recent methods. The performance accuracy rate for L-SVM, L-SVM-Wo, KSVM, K-SVM-Wo, CS, CS-Wo were obtained 98, 98.5, 95, 94.5, 98.1, and 98.3
respectively, while the proposed method is obtained as 99.58.
Açıklama
Anahtar Kelimeler
Face Recognition, Imperfect Face Data, Wavelet Transform, Fractal Model, MPM-LSTM
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Mahmood, Baraa Adil. (2023). An investigational FW-MPM-LSTM approach for face recognition using defective data. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
