Ritchie, K. L.White, D.Kramer, R. S. S.Noyes, EilidhJenkins, R.Burton, A. M.2019-07-122019-07-122018-08-170888-4080https://hdl.handle.net/10735.1/6690Economic and Social Research Council, Grant/Award Number: ES/J022950/1. FP7: European Research Council, Grant/Award Number: 32326Full text access from Treasures at UT Dallas is restricted to current UTD affiliates (use the provided link to the article). Non UTD affiliates will find the web address for this item by clicking the Show full item record link and copying the "relation.uri" metadata.Low-quality images are problematic for face identification, for example, when the police identify faces from CCTV images. Here, we test whether face averages, comprising multiple poor-quality images, can improve both human and computer recognition. We created averages from multiple pixelated or nonpixelated images and compared accuracy using these images and exemplars. To provide a broad assessment of the potential benefits of this method, we tested human observers (n = 88; Experiment 1), and also computer recognition, using a smartphone application (Experiment 2) and a commercial one-to-many face recognition system used in forensic settings (Experiment 3). The third experiment used large image databases of 900 ambient images and 7,980 passport images. In all three experiments, we found a substantial increase in performance by averaging multiple pixelated images of a person's face. These results have implications for forensic settings in which faces are identified from poor-quality images, such as CCTV.en©2018 John Wiley & Sons, Ltd.AverageClosed-circuit televisionFace perceptionDigital images--DeconvolutionarticleRitchie, K. L., D. White, R. S. S. Kramer, E. Noyes, et al. 2018. "Enhancing CCTV: Averages improve face identification from poor-quality images." Applied Cognitive Psychology 32: 671-680, doi:10.1002/acp.3449