Face recognition technology based on ResNet-50

Abstract

Face recognition technology is progressively finding its place across diverse domains. In pursuit of enhancing the efficacy of face recognition systems, this study employs a ResNet-50 deep convolutional neural network. The dataset is meticulously gathered and processed via OpenCV, thus amplifying the precision and utility of face recognition. ResNet, an advanced convolutional neural network, incorporates the concept of residual connections, bridging convolutional layers through shortcut connections. These connections facilitate the addition of input to output, forming residual blocks. Consequently, ResNet-50 efficiently tackles the vanishing gradient issue, enabling the training of exceptionally deep networks. With 49 convolutional layers and a fully connected layer, ResNet-50 boasts a robust architecture. To emulate varying brightness conditions, post-collection image adjustments are applied randomly. This strategy curbs the impact of divergent lighting scenarios on recognition accuracy, bolstering the models practical applicability. Notably, experimental outcomes underscore the commendable performance of the trained ResNet-50 model in face recognition trials. This substantiates the broad-spectrum viability of face recognition technology in domains such as security surveillance, human-machine interaction, identity verification, and beyond.