Yes, studies have shown that facial recognition technology performs worse on certain racial or gender groups in terms of accuracy. This is mainly due to the following reasons: 1. **Imbalanced training data**: The training data sets of many facial recognition systems mainly contain facial images of white people or certain specific races, which makes the system perform poorly when recognizing faces of other races. 2. **Algorithmic bias**: Some facial recognition algorithms may fail to take diversity into account during the development process, resulting in higher recognition error rates on certain groups. This bias is often ignored when designing and optimizing algorithms. 3. **Skin color and feature differences**: Facial recognition algorithms may have different sensitivities to facial features of different races in terms of lighting, contrast, and skin color, resulting in lower recognition accuracy. 4. **Social and cultural factors**: The application context of facial recognition technology, social and cultural differences, and user usage habits may also affect the performance of the technology. To improve these issues, researchers are pushing for more diverse training data sets and developing new algorithms to improve recognition accuracy for various races and groups. In addition, more and more discussions are focusing on algorithmic transparency, fairness, and possible bias to ensure the fair application of technology.