Research on Algorithms of Lateral Face Recognition Based on Data Generation

With the rapid progress of artificial intelligence, the methods of face recognition have also achieved considerable progress. However, when multiple head poses are present, the accuracy of face recognition decreases due to angular deviation. Therefore, how to realize the enhancement of accurate precision in two-dimensional multiple head poses is still a worthy research topic. In this paper, based on the ResNet50 residual network structure, data enhancement and attention mechanism are adopted, and the Public Figures Face Database public face database is used to partition the training and test sets. This hybrid approach effectively improved the accuracy of face recognition in lateral. However, the hybrid method has low accuracy for large angle deflection face recognition. Therefore, based on the hybrid method, this article proposes a side face recognition algorithm based on data generation, incorporating a data generation algorithm. The recognition accuracy of the front face of this algorithm is as high as 96.3%. This article presents a data-generated lateral face recognition algorithm that further improves the accuracy of large angle deflection face recognition.