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In near infrared face recognition systems, situations including head rotation, motion blur, darkness, eyes closed, mouth opened and the small face region will deteriorate the recognition accuracy. Thus, it is necessary to design a quality assessment system to select the best frame from the input video sequence before face recognition or saving it to database. In this paper we present a scoring evaluation system based on five features including sharpness, brightness, resolution, head pose and expression. Firstly, the score of each feature is computed independently, and then the final quality score is obtained by combining the scores of five features with weights. Center for Biometrics and Security Research (CBSR) Near Infrared Face Dataset is used to test the system. The experiment results demonstrate the effectiveness of the proposed quality assessment.