In order to improve the efficiency of facepiece-wearing detection for tunnel operation workers, a feature fusion-based facepiece detection model was proposed. First, high quality query images were selected, and an image gallery was established. An image retrieval method was adopted to obtain samples and measure the similarity between query and gallery images, thereby iteratively expanding the dataset scale. Then, Histogram of Oriented Gradients (HOG) and Fisher features were extracted from the images. The Ant Lion Optimizer (ALO) was introduced to compute the optimal weight combination for the two types of features, which were subsequently fused. Finally, based on the fused features, a SVM was utilized to train a facepiece detection model, and experimental evaluations were conducted on the self-constructed dataset. The results indicate that the proposed model effectively accomplishes the task of facepiece-wearing detection in tunnel operation scenarios. Feature fusion enhances the image description and improves the detection accuracy of the model. Compared to using only HOG features or Fisher features, the accuracy is increased by 6% and 14%, respectively. The model meets the accuracy requirements for facepiece-wearing detection of workers in tunnel construction environments.