Kurnaz, SeferMahmood, Baraa Adil2023-09-192023-09-1920232023Mahmood, Baraa Adil. (2023). An investigational FW-MPM-LSTM approach for face recognition using defective data. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.https://hdl.handle.net/20.500.12939/4018Facial 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.eninfo:eu-repo/semantics/closedAccessFace RecognitionImperfect Face DataWavelet TransformFractal ModelMPM-LSTMDoctoral Thesis806583