Experimental Analysis of Face Recognition on Still and CCTV Images

Although automatic identity inference based on faces has shown success when using high quality images, for CCTV based images it is hard to attain similar levels of performance. Furthermore, compared to recognition based on static images, relatively few studies have been done for video based face recognition. In this paper, we present an empirical analysis and comparison of face recognition using high quality and CCTV images in several important aspects: image quality (including resolution, noise, blurring and interlacing) as well as geometric transformations (such as translations, rotations and scale changes). The results show that holistic face recognition can be tolerant to image quality degradation but can also be highly influenced by geometric transformations. In addition, we show that camera intrinsics have much influence - when using different cameras for collecting gallery and probe images the recognition rate is considerably reduced. We also show that the classification performance can be considerably improved by straightforward averaging of consecutive face images from a CCTV video sequence.