A flexible hierarchical approach for facial age estimation based on multiple features

• Published in: Pattern recognition Vol. 54; pp. 34 - 51
• Main Authors: Pontes, Jhony K., Britto, Alceu S., Fookes, Clinton, Koerich, Alessandro L.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2016
• Subjects: Active appearance models; Age; Age determination; Age estimation; Classification; Errors; Face recognition; Facial; Human; Local phase quantization; Regression; Robustness; Support vector machines; Regression; Local phase quantization; Age estimation; Face recognition; Active appearance models; Classification
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2015.12.003

Age estimation from facial images is increasingly receiving attention to solve age-based access control, age-adaptive targeted marketing, amongst other applications. Since even humans can be induced in error due to the complex biological processes involved, finding a robust method remains a research challenge today. In this paper, we propose a new framework for the integration of Active Appearance Models (AAM), Local Binary Patterns (LBP), Gabor wavelets (GW) and Local Phase Quantization (LPQ) in order to obtain a highly discriminative feature representation which is able to model shape, appearance, wrinkles and skin spots. In addition, this paper proposes a novel flexible hierarchical age estimation approach consisting of a multi-class Support Vector Machine (SVM) to classify a subject into an age group followed by a Support Vector Regression (SVR) to estimate a specific age. The errors that may happen in the classification step, caused by the hard boundaries between age classes, are compensated in the specific age estimation by a flexible overlapping of the age ranges. The performance of the proposed approach was evaluated on FG-NET Aging and MORPH Album 2 datasets and a mean absolute error (MAE) of 4.50 and 5.86 years was achieved respectively. The robustness of the proposed approach was also evaluated on a merge of both datasets and a MAE of 5.20 years was achieved. Furthermore, we have also compared the age estimation made by humans with the proposed approach and it has shown that the machine outperforms humans. The proposed approach is competitive with current state-of-the-art and it provides an additional robustness to blur, lighting and expression variance brought about by the local phase features. •A highly discriminative feature representation, which is able to model shape and appearance as well as wrinkles and skin spots.•A novel hierarchical method consisting of a multi-class SVM and a SVR.•The errors are compensated in the detailed age estimation by overlapping flexibly the age ranges of each age function.•Experiments have been carried out on the publicly available FG-NET Aging and MORPH Album 2 datasets.•An increased robustness to blur, lighting and expression variance through local phase features.