The Research On Face Image Quality Assessment

Automatic face recognition has great potential applications in public security, intelligent surveillance, digital personal identify, electronic commerce, multimedia, digital entertainment, etc. and has great theory value in many subjects, so face recognition has attracted much research attention from the research institutes, governments, military and security departments. Over the past 30 years, great progress and developments have been made in face recognition. Now under the controlled and cooperative conditions, face recognition systems perform very well, but under uncontrolled and uncooperative conditions, especially when the illumination in face images and facial poses are variant. For the wide use of face recognition, ISO/IEC established a group to draft standards about face image. One of them is to evaluate the aspects which influent system performance.In 2006, CBSR(Center for Biometrics and Security Research) summit a standard draft on face image quality to ISO/IEC on behalf of China. In February 2007, the working draft was published. After some discussion and modification, the standard was passed in May 8. 2007. The author of this article, which is partner of this standard, will give a detailed introduction on this standard in this article. Also this paper proposes some algorithms to evaluate non-frontal lighting and improper facial pose.Before the standard was summit, some standards about fingerprint quality were established. Fingerprint is a touchable biometric feature, the conditions of capturing fingerprint image are under control. So fingerprint image quality evaluation is mostly about the effect of features. But for face recognition, the aspects that influent image quality are diversify because face recognition is a un-touchable biometric feature. So how to establish a standard for face image is difficult. On the other hand, for a face image, how to give an overall evaluation, there are no research on this problem. For this problem, this paper proposes a standard framework to evaluation the aspects that affect face image quality. First, some definition about quality evaluation are defined. Then we classify these aspects into some class. For most of the aspects, we give the evaluation methods in this paper. This gives a basement for future work on face image quality evaluation. Until now, most evaluation algorithm is to evaluation one aspects, so we propose an aspects-score and over-score method to give face image an overall evaluation.Based on the past research, non-frontal lighting and improper facial pose are the most important aspects that affect the system performance. For these two aspects, researchers proposes many algorithms to weaken the influence of them, but no big progress was made. These two problem still have not been solved. So the standard need to give evaluation for these problems. For this reason, this paper propose a method which use symmetry, a statistical feature of face to evaluate non-frontal lighting and improper facial pose. To describe symmetry, this paper use local windows to evaluate the symmetry of face image. This idea is came from LBP. After evaluation of symmetry, we give the methods to assess non-frontal lighting and improper facial pose.From the ISO stands, image quality must be directly connected with matching performance. But until now, there are no database with standard image quality. So most image quality methods used the quality marked by hand, this is not proper. For the problem of image quality can't been directly connected with matching performance, this paper uses regression method to build models between face image and genuine matching score. For the face images with non-frontal lighting and pose variation, first we evaluate the input image's symmetry, then build models between face image symmetry and matching score. Two regression methods are discussed, the first one, which selects some most effective features using Adaboost, build a linear model between these features and matching score. The second one is a non-linear method based on boosting. The experiment result show that these two methods can predict matching score very well.