Abstract:Aiming at the problem that low-quality face images hinder the performance improvement of recognition systems, we propose a no reference face image quality evaluation method to evaluate the impact of different types of image degradation on the quality of face images. This method uses a cluster convolutional network structure to simulate the feature offset in the face degradation process, and face image score is calculated based on the correlation between the feature offset and the amount of image information. The genetic algorithm is used to select the network units, so that the same performance can be achieved with a smaller network scale. Using the evaluation algorithm as a tool for experiments, the impact of different image degradation types on face recognition is evaluated and studied, some useful conclusions are drawn for guiding future research on face quality. Experiments conducted on mainstream face data sets show that by filtering low-quality face images in the database, the performance of the existing face recognition system can be further improved, and the improvement of the recognition rate shows good stability. This method is low in complexity and does not require training. Compared with the latest methods such as FaceQNet, it shows obvious advantages in FNMR and EER indicators.