The main challenge in deceiving face recognition (FR) models lies in the target model under the black-box setting. Existing works seek to generate adversarial examples to improve the adversarial transferability for black-box attacks. However, the attack performance and quality of the crafted image still have room for improvement. In this work, we propose a novel method called TransMix to improve the transferability of adversarial face examples based on data augmentation. Our approach leverages the mixture of the original image with a mixed sample image that is randomly mixed using images from different identities or the same identities, incorporating information from diverse categories. Then, we perform random transformations times to create diverse input patterns, exploiting the gradient from various images and other identities in the same iteration. Extensive experiments conducted on the CelebA dataset demonstrate that TransMix achieves a significantly higher attack success rate against different FR models and Vision Transformers (ViTs), outperforming the best competitor by a large margin of 5.6% and 8.8% when attacking the ViTs using adversarial images generated on the ArcFace model. Our results also confirm that adversarial examples crafted by TransMix exhibit good adversarial transferability against defense models, achieving an attack success rate of 52.3% on the Bit-Red model.