Combined local and holistic facial features for age-determination

This paper presents an advanced age-determination technique that combines holistic and local features derived from an image of the face. A 30×1 Active Appearance Model (AAM) linear encoding of each face is produced to work as holistic features. Meanwhile, local features are extracted by using Local Ternary Patterns (LTP). These combined features are used to classify faces into one of two age groups (age-classification). An age-determination function is then constructed for each age group in accordance with physiological growth periods for humans - pre-adult (youth) and adult. Compared to published results, this method yields the highest accuracy rates in overall mean absolute error (MAE), mean absolute error per decade of life (MAE/D), and cumulative match score.