Aiming at the problem of driver facial fatigue detection, a driver fatigue detection algorithm based on genetic algorithm optimized probabilistic neural network (PNN) was proposed. The face detector based on HOG feature is used to detect the face, and ERT algorithm is used to locate the key points. The four fatigue characteristic parameters including PERCLOS value, blink frequency, the proportion of yawning time per unit time and the frequency of nodding were calculated and input into PNN for fatigue discrimination, and the genetic algorithm was used to optimize the smoothing factor of PNN. Improve the accuracy of fatigue classification. NHTU-DDD dataset and YawDD dataset were used to train the network, and self-collected samples were used to verify the generalization performance of the model. Compared with SVM, BP neural network and unoptimized PNN model, the accuracy rates of SVM, BP neural network and unoptimized PNN were 95.67%, 97.67% and 95.33%, respectively. The accuracy of the proposed optimized PNN model is 98.67%, which verifies the effectiveness of the proposed algorithm.