It is technically feasible to develop a more effective facial recognition system to track specific individuals using deep learning technology, but it also faces many complex problems and challenges. From a technical perspective, deep learning can significantly improve the accuracy, speed and robustness of facial recognition, and can better capture and analyze facial features, thereby more accurately identifying specific individuals. By continuously optimizing the model architecture, increasing the amount of data and improving the algorithm, a better-performing system can be developed to achieve the function of tracking specific individuals. However, this also brings a series of serious problems: 1. **Privacy violation**: Excessive tracking of specific individuals seriously infringes on the privacy of others and may lead to excessive surveillance of personal lives. 2. **Risk of abuse**: There is a possibility of malicious use, such as for illegal monitoring, tracking and other bad behaviors. 3. **Social ethical issues**: It may cause public concern about personal freedom and safety, and may affect the trust structure of society. Therefore, when developing and applying such technologies, it is necessary to carefully weigh and ensure the reasonable, legal and safe application of technology under the premise of following strict laws, regulations and ethical standards to avoid negative impacts on individual rights and social order.