Improved Face Recognition Rate Using Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have shown good performance in the domain of face recognition due to their capability of extracting discriminative features. In this paper, we present a face recognition system where a Multi-Task Convolutional Neural Network (MTCNN) is employed for face detection and preprocessing. Afterwards, we use the proposed model of CNN with optimization and a softmax function as a classifier for recognition. Experiments have been carried out on the ORL face database, which consists of 400 images for 40 classes. The results of the implementation illustrate that our model has achieved better performance compared to most of the state-of-the-art models, with an accuracy rate of 97.50%.