Metaheuristic Approaches for Occlusion Invariant 3D Face Recognition Technique

Abstract

Face identification is one of the non - intrusive biometric strategies. Over the decades, several investigators have sought to achieve state-of-the-art face-recognition frameworks. Recently, AlexNet has proven to be the beginning of the deep learning age. Many face-recognition issues have been overcome in the last eight years by using deep learning methods ranging from multilayer perceptron to convolutional neural networks. Transfer learning methods have also been handy where the dataset is not huge, and we have a small number of subjects. Researchers are focusing on 3D facial recognition and restoration for the last 3-4 years. There are various methods for 3D face reconstruction, namely, morphable model-based reconstruction, epipolar geometry-based reconstruction, and one-shot learning-based reconstruction, shape from shading-based reconstruction, as well as deep learning-based reconstruction. 3D face reconstruction techniques using voxels as well as facial landmarks have been proposed in this work. There are three face reconstruction techniques proposed in this work viz. 3D voxel-based face reconstruction using sequential deep learning, 3D landmarks-based face reconstruction, and voxel-based occlusion-invariant face recognition using game theory and simulated annealing. The three datasets, namely, Bosphorus Database, University of Milano Bicocca 3D Face Database, and Kinect Face Database, have been used for training and testing phases. Using the game theory and simulated annealing method, the overall classification accuracy obtained from voxel-based facial recognition is 86.1%. For occlusion invariant facial recognition, the average accuracy generated by the proposed technique is 75.5%. In facial recognition using 3D landmarks, the average accuracy achieved from the given methodology is 81.3%. In the case of 3D mesh face recognition, the average precision achieved from the methodology is 83.9%. Adversarial generation technique for triplet generation promotes minimal bias. The technique coupled with simulated a