Fuzzy Discriminant Analysis: Considering the Fuzziness in Facial Age Feature Extraction

Abstract

In traditional age estimation methods which utilize discriminative methods for feature extraction, the biological age labels are adopted as the ground truth for supervision. However, the appearance age, which is indicated by the facial appearance, is intrinsically a fuzzy attribute of human faces which is inadequate to be labeled as a crisp value. To address this issue, this paper firstly introduces a fuzzy representation of age labels and then extends the LDA into fuzzy ones. In the definition of fuzzy labels, both the ongoing property of facial aging and the ambiguity between facial appearance and biological age are considered. By utilizing the fuzzy labels for supervision, the proposed method outperforms the crisp ones in both preserving ordinal information of aging faces and adjusting the inconsistency between the biological age and appearance. Experiments on both FG-NET and MORPH databases confirm the effectiveness of the proposed method.

Keywords: Age information, Aging pattern subspace, Appearance age, Biological age, Conformal embedding analysis, Cumulative score, Facial age estimation, Facial appearance, Facial landmark, Facial shape, Fuzziness in Facial aging, Fuzzy discriminant analysis, Fuzzy LDA, Fuzzy representation of age labels, Intra-class and inter-Class neighbors, Linear discriminant analysis, Marginal Fisher analysis, Mean absolute error, Ordinary preserving LDA, Ordinary preserving MFA.