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Face Recognition in Multiple Variations Using Deep Learning and Convolutional Neural Networks

Face Recognition (FR) has been widely used in the tracking and identification of individuals. However, because face images vary depending on expressions, ages, individual locations, and lighting conditions, the facial photographs of the same sample may appear to be distinct, making face recognition more difficult. Deep learning (DL) is now a suitable solution for face recognition and computer vision. In this study, features and traits were extracted from images of a large data set (called FERET) consisting of 14,126 images that were divided into 80% for training data and 20% for testing data using a Convolutional Neural Network (CNN). The CNN is first pre-trained using supplementary data for the purpose of obtaining updated weights, and then trained with the target dataset in order to uncover more hidden facial characteristics. Three different deep learning models are implemented: AlexNet, Resnet18, and DenseNet-161. The performance of these models is compared experimentally in terms of their classification accuracy. The obtained results showed that the DenseNet-161 has the highest accuracy of 98.6%, while the accuracies of the Resnet18 and AlexNet are 96.3% and 93.3%, respectively.