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Motion blur, out of focus, insufficient spatial resolution, lossy compressionand many other factors can all cause an image to have poor quality. However,image quality is a largely ignored issue in traditional pattern recognitionliterature. In this paper, we use face detection and recognition as casestudies to show that image quality is an essential factor which will affect theperformances of traditional algorithms. We demonstrated that it is not theimage quality itself that is the most important, but rather the quality of theimages in the training set should have similar quality as those in the testingset. To handle real-world application scenarios where images with differentkinds and severities of degradation can be presented to the system, we havedeveloped a quality classified image analysis framework to deal with images ofmixed qualities adaptively. We use deep neural networks first to classifyimages based on their quality classes and then design a separate face detectorand recognizer for images in each quality class. We will present experimentalresults to show that our quality classified framework can accurately classifyimages based on the type and severity of image degradations and cansignificantly boost the performances of state-of-the-art face detector andrecognizer in dealing with image datasets containing mixed quality images.

Quality Classified Image Analysis with Application to Face Detection and Recognition