Face Recognition Using Imperfect Data with FW-MPM-LSTM Method

Intelligent detection systems are nowadays used as an important issue in various parts, especially security, business, administrative, economic, military, public, etc., and cover a wide range. Therefore, proposing new solutions to cover the previous challenges is a critical issue. One of these intelligent detection systems is the face recognition from images or videos that the user can use from popular entertainment on mobile phones to super-security systems. 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. 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.

Keywords:

Face Recognition , Imperfect Face Data , Wavelet Transform , Fractal Model , MPM-LSTM