Recognition Of Facial Images Using Convolution Based Multilayer Perceptron Model

In this paper, C-MLP based Network is used to restore the image and recognize the facial images, for the purpose of resolving the face recognition issue when the facial images are severely occluded. Multilayer perceptrons (MLPs) provide the ability to effectively represent intricate, non-linear associations within datasets. In Face recognition techniques, faces can manifest diverse non-linear fluctuations as a consequence of factors such as variations in lighting conditions, facial emotions, and position. Multilayer perceptrons (MLPs) have the capability to acquire the ability to detect and represent these detailed patterns, rendering them well-suited for processing the intrinsic intricacy present in facial data. The ICA-NNMF feature extraction approach is employed in this model to extract features, and Convolution based MLP is deployed to recognize faces. Users must modify the reconstructing result in compliance to their particular interests or the properties of the restored image since the perceptual quality of the image is more or less subjective. With this approach, users can regulate the continuous transition between several objectives, such as the trade-off between noise reduction and detail preservation. This model ensures the normal distribution along with parametric optimization contributing to the high performance for restored face recognition. Thus, It is analyzed and described how well the framework performs when correctly identifying faces in the generated images.