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2012 IEEE Workshop on the Applications of Computer Vision
Predicting good, bad and ugly match Pairs
Breckenridge, CO USA
January 09-January 11
ISBN: 978-1-4673-0233-3
K. W. Bowyer, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
P. J. Flynn, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
S. Biswas, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
G. Aggarwal, Dept. of Comput. Sci. & Eng., Univ. of Notre Dame, Notre Dame, IN, USA
Several sources of variation in facial appearance that affect face matching performance have long been investigated. The recently introduced GBU challenge problem [1] indicates that there can be significant variation in performance across different partitions of the data, even when the impact of most known factors is eliminated or significantly reduced by the data collection and experimentation protocol. The GBU challenge problem consists of three partitions which are called the Good (easy to match), the Bad (average matching difficulty) and the Ugly (difficult to match). In this paper, we investigate various image and facial characteristics that can account for the observed significant difference in performance across these partitions. Given a match pair, we aim to predict the partition it belongs to. Partial Least Squares (PLS)-based regression is used to perform the prediction task. Our analysis indicates that the match pairs from the three partitions differ from each other in terms of simple but often ignored factors like image sharpness, hue, saturation and extent of facial expressions.
Index Terms:
Face,Measurement,Vectors,Partitioning algorithms,Face recognition,Algorithm design and analysis,Image edge detection
Citation:
K. W. Bowyer, P. J. Flynn, S. Biswas, G. Aggarwal, "Predicting good, bad and ugly match Pairs," wacv, pp.153-160, 2012 IEEE Workshop on the Applications of Computer Vision, 2012
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