(Spring 2013) Impact of Facial Aging on Image Quality

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The purpose of this research is to provide an evaluation of how facial aging has an effect on image quality. Facial recognition is a growing field in biometrics; it is important to distinguish what characteristics of the face can change without making an impact on the facial recognition system. To determine this, we used the following variables: Eye Separation, Eye Axis Angle, Eye Axis Location, Facial Dynamic Range, and Percent Facial Brightness.

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(Spring 2013) Impact of Facial Aging on Image Quality

  1. 1. IMPACT OF FACIAL AGING ON IMAGE QUALITY The purpose of this research is to provide an evaluation of how facial aging has an effect on image quality. Facial recognition is a growing field in biometrics; it is important to distinguish what characteristics of the face can change without making an impact on the facial recognition system. To determine this, we used the following variables: Eye Separation, Eye Axis Angle, Eye Axis Location, Facial Dynamic Range, and Percent Facial Brightness. Amol Gharte, John Shiver, Peters Drey, Michael Brockly, Stephen Elliott Overview Facial Dynamic Range is the ratio of lightest to darkest pixel of the image in comparison to the average population. This variable is important in explaining the impact of facial aging on image quality because it is one of the key attributes that a biometric device will take into account when making a pass or fail decision on facial comparisons. The table, Different Variables in Facial Aging, shows variables such as eye separation, facial brightness, eye axis location, and eye axis angle are all inconsistent from each time lapse sample. This set of images is an example of a time lapse series. The woman had taken a photo everyday for 5.5 years and the above photos are samples from randomly chosen days. Video Eye Separation Facial Brightness Facial Dynamic Range Eye Axis Location Eye Axis Angle 1 Year I D D D I 8 Months D D N I N 6 Years D I N D D 5.5 Year N N N N N 8 Years N N N N N Increasing Trend (I), Decreasing Trend (D), No Trend (N) 1 year8 months 6 years 5.5 years 8 years After taking into account five key variables our studies showed that facial dynamic range was the most consistent over time. Our research showed this through the graphs because the slopes of the facial dynamic range are fairly stable compared to the other variables. In conclusion, this study has proven that there is no impact of facial aging on image quality. Facial Dynamic Range The graphs are results of studies from five different individuals who took time lapse photos ranging from eight months to eight years. These are indicators of how stable facial dynamic range is in comparison to other variables. Video Incremented Time Between Picture Amount of Photos 8 Month Every week 28 8 Years N/A 1470 5.5 Years daily 638 6 Years N/A 880 1 Year daily 366 Different Variables in Facial Aging Conclusion Time Lapse Information

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