Kernel spectral regression of perceived age from hybrid facial features

This paper introduces an advanced age-determination technique using hybrid facial features and Kernel Spectral Regression, a nonlinear dimensionality reduction method. In the preprocessing stage, the logarithmic nonsubsampled contourlet transform (NSCT) is conducted to denoise and amplify facial wrinkles that help to distinguish young faces from elder ones. Then the hybrid facial features that combine both local and holistic features are extracted from the preprocessed images. Our novel Uniform Local Ternary Patterns (ULTP) are used as the local features. Meanwhile the holistic features are extracted by using the Active Appearance Model (AAM) to encode each face. Kernel Spectral Regression is used to minimize inter-class distances while maximizing intra-class distances of feature sets. These reduced features are used to classify faces into 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 promising results in overall mean absolute error (MAE), mean absolute error per decade of life (MAE/D), and cumulative match score in various face aging corpuses.