Face recognition based on convolutional neural network fusion with inception module

Nowadays, face recognition technology has widely penetrated into various industries, however, there are still multiple factors that challenge the recognition effectiveness of the model. To this end, this paper innovatively introduces a novel network architecture that incorporates convolutional neural networks and Inception modules. In this architecture, we use a 1×1 convolutional layer for dimension boosting operation, while multiple 3×3 convolutional units as well as the dilated convolutions technique are employed in parallel to fully capture the feature information of face images under different receptive fields. Considering the intrinsic similarity between face images, a joint loss function combining the Softmax loss function and the Center loss function is specially designed in this study, which is used as a benchmark for evaluating the model during the backpropagation process. After a series of rigorous experimental validation, this improved scheme shows significant effect enhancement and confirms its superiority in the field of face recognition.