Face recognition method based on convolutional neural network and distributed computing

Abstract Face attribute recognition is also widely used in human–computer interaction, train stations, and other fields because face attributes are rich in information. A convolutional neural network and distributed computing (DC)-based face recognition model is researched to enhance multitask face recognition accuracy and computational capacity. The model is also trained using a multitask learning method for face identity recognition, fatigue state recognition, age recognition, and gender recognition. The model is streamlined by DC to improve the computational efficiency of the model. To further raise the recognition accuracy of each task, the study combines the add feature fusion (FF) principle and concat FF principle to propose an improved face recognition model. The outcomes of the study revealed that the true acceptance rate value of the model reached 0.95, and the face identity recognition accuracy reached 100%. The fatigue state determination accuracy reached 99.12%. The average recognition time of a single photo was 354 ms. The model can quickly recognize face identity and face attribute information in a short time, with short time and high recognition accuracy. It is beneficial in several areas, including intelligent navigation and driving behavior analysis.