Face image quality assessment based on photometric features and classification techniques

Quality of face images may be degraded as they are captured under varying capturing conditions such as illumination and speed of moving subject in videos. Performance of a typical face recognition systems is sensitive to the quality of input face images. Face image quality assessment is necessary for accurate face recognition systems both in the enrollment and recognition stages. Face image quality assessment is considered as a complex task as some of quality factors are in contrast to each other in different environmental conditions. In this study, a face image quality assessment based on photometric quality factors using classification techniques is proposed to justify applicability of used quality factors. The proposed method has three main phases namely, quality factor measurement, feature normalization, and classification. Evaluation of the proposed method on modified NLPR face dataset demonstrates all of the used classifiers have almost equal performance but, MLP classifier outperforms other classifiers in terms of f-score and accuracy measures slightly. Experimental results revealed that brightness, contrast, focus, and illumination are effective factors for purpose of still face image quality assessment. The proposed method also has better performance with comparison with some of the existing methods based on the mentioned dataset.