An investigational FW-MPM-LSTM approach for face recognition using defective data

Abstract

Facial recognition systems are based on the features and traits of the face, since the systems are classified as biometric systems. Additionally, they are founded on the image processing, machine vision and machine learning principles. From images, imperfect information is considered by face recognition systems. A variety of image reconstruction mechanisms is vital in this situation in order to match faces. The proposed method calls for image enhancement at the pre-processing stage. Following the image segmentation and reconstruction stage, the best facial features are extracted using features such the eyes, cheeks, face area and lips. By means of fractal model and wavelet transform the operation is performed. Using the Moore Penrose Matrix, the LSTM neural network is then improved also known as the MPM-LSTM, to train and test the system. From experimental results, the outcomes show that the proposed methodology performs better than the contemporary techniques.