Abstract-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-theart 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 pretrained 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. There are many existing algorithms handles the human face recognition. The purpose of these algorithms is to achieve an optimal recognition rate or near optimal recognition rate in realtime processing time. However, none of the algorithms achieved a100% recognition rate. Therefore, human face recognition is an incredibly exciting field for researchers. The neural network classifier is relying on robust features extraction methods. Neural network performs well with a low error rate by feeding strong distinction patterns and features. This research is motivated by the drawbacks and limitations of existing systems. Existing methods relays on simple preprocessing methods such as face detection and simple image registration. Therefore, we felt that there is a potential improvement can be achieved by implementing strong image registration based on deep learning approach and a strong features extraction approach which can provide strong distinction patterns of the features based on the existing feature extraction methods such as PCA and LBP. Using five distance methods and combining them will provide a clear pattern which helps the NN to converge faster and more accurate. Obtaining the T-Set based on the correlation between the training dataset will provide a robust training data which we used as an input of the NN. Each distance method performs well in a different direction. Therefore, adding a strength factor helps to improve the accuracy rate. The proposed framework is divided into five steps as shown in Fig.1. 1) Preprocessing step: Face recognition needs huge storage and CPU resources. Therefore, we applied a few of the preprocessing operations to reduce the computing time. Haar-cascade detection is used to detect the face then we cropped the face to reduce the background effect. We converted the images to a grayscale image then we applied a histogram equalizer to reduce the noise effect. Finally, we resized the images to the size we preferred. 2) Features extraction: We used the PCA algorithm to reduce the dimensionality of the images by eliminating the redundant data between the training images while retaining the variation between them. The PCA is transforming the dataset into a new set of variables which called the principal components (PCs). The first PC retains the maximum variation in the dataset. The PCA sorts the Eigen-Vectors and selects to top K values to reduce the dimensions. The training dataset must be scaled, and the complexity of calculating the covariance matrix are some of the drawbacks of the PCA. However, we used the PCA to prove that there is a potential accuracy improvement 978-1-5386-9120-5/18/$31.00 ©2018 IEEE
In terms of computational power is the reduction in data and function in the face recognition process essential and the researchers have recently been focused on the use of modern neural networks for automated analysis REF .