Improvements and Performance Evaluation Concerning Synthetic Age Progression and Face Recognition Affected by Adult Aging

Authors:
Amrutha Sethuram;Eric Patterson;Karl Ricanek;Allen Rawls
Affiliations:
Face Aging Group, Computer Science Department, UNCW, USA;Face Aging Group, Computer Science Department, UNCW, USA;Face Aging Group, Computer Science Department, UNCW, USA;Face Aging Group, Computer Science Department, UNCW, USA
Venue:
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Year:
2009

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Abstract

Aging of the face degrades the performance of face recognition algorithms. This paper presents recent work in synthetic age progression as well as performance comparisons for modern face recognition systems. Two top-performing, commercial systems along with a traditional PCA-based face recognizer are compared. It is shown that the commercial systems perform better than the baseline PCA algorithm, but their performance still deteriorates on an aged data-set. It is also shown that the use of our aging model improves the rank-one accuracy in these systems.