Aging effects in automated face recognition
The main objective of this work was to analyze the effects of aging on the automated face recognition process.^ A dataset was used to perform experiments and obtain indicators to measure the impact of aging. To compare the effects of aging the dataset was segmented based on the age difference between the subjects’ face images. The image quality metrics were also part of the analysis performed in this study. ^ The results of the experiments shown that the higher the gap between the images, the higher the error rates. These were the expected results and it is consistent with other experiments performed in the past. The False Rejection Rate (FRR) was measured at 1%, 0.1%, and 0.01% False Acceptance Rate (FAR) obtaining the similar output as the gap between the images increased. ^
Stephen J. Elliott, Purdue University.
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