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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.

Normal adult aging in the face can drastically affect performance of face recognition systems. Synthetically generating age-progressed or age-regressed images for aiding recognizers is one method of improving the robustness of facebased biometrics. These synthetic age progressions may also aid human law enforcement and other applications. There has been wide interest in these techniques in recent years, and the use of Active Appearance Models (AAMs) for synthetic age progression has been shown to be a promising approach but has not yet been demonstrated on a large human population with wide variation. This paper presents improvements in AAMbased age progression that generate significantly improved visual results, taking into account a much wider gender, age, and ethnic range than published to date for age progression techniques.

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