An Advanced Ensemble Directionality Pattern (EDP) based Block Ensemble Neural Network (BENN) Classification Model for Face Recognition System

Developing the face detection and recognition system is one of the most demanding and challenging tasks in the recent days. For this purpose, there are different types of image processing techniques have been developed in the conventional works, which are mainly focusing on detecting the facial images under occlusions. Still, it limits with the major problems of high complexity in algorithm design, does not have the ability to handle large dimensional data, requires more time consumption for training the model, and increased misclassification results. Thus, this paper intends to develop an efficient face recognition system for detecting the occluded faces by implementing an advanced image processing techniques using COFW-100 dataset which includes 507 occluded images. Here, the Gaussian filter technique is utilized to improve the overall quality of original image by suppressing the noise/artifacts. After that, an Ensemble Directionality Pattern (EDP) extraction technique is applied to extract the texture features from the normalized image by estimating the similarity between the pixel intensity in different angles of projection plane. It is mainly used to obtain the clear features of the person in the cell of each image frame and, this type of feature extraction helps to increase the accuracy of overall classification system. Then, the Block Ensemble Neural Network (BENN) classification model is deployed to accurately detect the occluded faces by processing the image with separate patterns of features. During experimentation, the performance of both existing and proposed techniques is validated and compared by using various evaluation metrics. Then, the results show that the proposed technique outperforms the other approaches by accurately detecting the face based on the image patterns with separate blocks.