Improving Human Face Recognition using Deep Learning based Image Registration and Multi-Classifier Approaches

Face recognition has become a fascinating field for researchers. The motivation behind the enormous interest in the topic is the need to improve the accuracy of many real-time applications. The complexity of the human face and the changes due to different effects make it more challenging to design as well as implement a powerful computational system for human face recognition. In this work, we presented an enhanced approach to improve human face recognition using a Back-Propagation Neural Network (BPNN) and features extraction based on the correlation between the training images. A key contribution of this work is the generation of a new set called the T-Dataset from the original training dataset, which is used to train the BPNN. We generated the T-Dataset utilizing the correlation between the training images without using a conventional technique of image density. The correlated T-Dataset provides a high distinction layer between the training images, which helps the BPNN to converge faster and achieve better accuracy. Data and features reduction is essential in the face recognition process, and researchers have recently focused on the modern Neural Network (NN). Therefore, we used a Principal Component Analysis (PCA) descriptor to prove that there is a potential improvement even using traditional methods. We applied five distance measurement algorithms and then combined them to obtain the T-Dataset, which we fed into the BPNN. We achieved higher face recognition accuracy with less computational cost compared to the current approach by using reduced image features. We test the proposed framework on two small datasets, the YALE and AT&T datasets, as the ground truth. We achieved tremendous accuracy. Furthermore, we evaluate our method on one of the state-of-the-art benchmark datasets, Labeled Faces in the Wild (LFW), where we produce a competitive face recognition performance. Also, we proposed an enhanced framework to improve the face registration using deep transfer learning. We used pre-trained models such as ResNet50 and SeNet50 which trained on the VGGFace2 dataset. We used the LFW dataset to evaluate our method, and we achieve high accuracy.