Improving Borderline Adulthood Facial Age Estimation through Ensemble Learning

2 Jul 2019Felix AndaDavid LillisAikaterini KantaBrett A. BeckerElias Bou-HarbNhien-An Le-KhacMark Scanlon

Achieving high performance for facial age estimation with subjects in the borderline between adulthood and non-adulthood has always been a challenge. Several studies have used different approaches from the age of a baby to an elder adult and different datasets have been employed to measure the mean absolute error (MAE) ranging between 1.47 to 8 years... (read more)

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