Improving Audience Analysis System Using Face Image Quality Assessment

Abstract

Video surveillance has a wide variety of applications for indoor and outdoor scene analysis. The requirements of real-time implementation with the desired degree of recognition accuracy are the main practical criteria for most vision-based systems. When a person is observed by a surveillance camera, usually it is possible to acquire multiple face images of a single person. Most of these images are useless due to the problems like the motion blur, poor illumination, small size of the face region, 3D face rotation, compression artifacts, and defocusing. Such problems are even more important in modern surveillance systems, where users may be uncooperative and the environment is uncontrolled. For most biometric applications, several of the best images are sufficient to obtain the accurate results. Therefore, there is a task for develop a low complexity algorithm, which can choose the best image from a sequence in terms of a quality. Automatic face quality assessment can be used to monitor image quality for different applications, such as video surveillance, access control, entertainment, video-based face classification, and person identification and verification. In practical situation, the normalized images at the output of face detection algorithm are post-processed and their quality is evaluated. Low quality images are discarded and only images with acceptable qualities are received for further analysis. There are several algorithms for face quality assessment that are based on estimating facial properties, such as the estimating the pose, calculating the asymmetry of the face, and non-frontal illumination to quantify the degradation of a quality. Several investigations show that application of a quality assessment component in video-based face identification system can significantly improve its performance. Another possible application of face quality assessment algorithm is to process the images with different qualities in different ways. Proposed face quality assessment method has been applied as a quality assessment component in video-based audience analysis system. Using the proposed quality measure to sort the input sequence and taking only high quality face images, we successfully demonstrated that it not only increases the recognition accuracy but also reduces the computational complexity.