Abstract: Laboratory utilization rate is an important indicator of laboratory management, but the statistics of laboratory and equipment usage in colleges and universities are still mainly relying on manual record. By combining face recognition technology, quick response code recognition technology and Internet of Things technology, the laboratory utilization rate can be obtained in real time and the management of laboratory personnel and equipment can be realized. At the same time, aiming at the problem of non-uniform illumination in laboratory, the image light can be corrected by an illumination self-adaptive adjustment algorithm based on improved two-dimensional gamma function, which greatly improves the recognition accuracy under non-uniform light. The deployment of tests in the laboratory proves that the identification of personnel and the equipment borrowed/returned can be achieved. According to the data, the utilization rate counted of the laboratory is proved to be a remarkable achievement. This system is helpful for the existing laboratory management in colleges and universities.