Lightweight New Fesidual Face Recognition Method Based on Feature Fusion

Aiming at the problem that the existing lightweight models are difficult to improve the recognition accuracy in the face recognition applications of embedded devices, a new lightweight residual network model (Lightweight New Residual Network, LNRN) that integrated the key feature point information of face alignment is proposed. The advantage of deep residual network structure that can solve the network degradation and avoid the influence of interference factors are absorbed by LNRN. In order to realize the extraction of key feature information and global information after combining the key point information generated by the face alignment, the deep residual network structure is simplified and reasonably designed. In order to avoid losing important feature information in the process of feature extraction, an attention mechanism combining space and channel is added to the new residual network for assistance. Simulation experiments on the four standard face datasets showed that the recognition speed of the proposed model was close to the mainstream lightweight face methods, and the average recognition accuracy of the proposed model is 0.6% higher than that of MobiFace. © 2024 Chinese Institute of Electronics. All rights reserved.