Abstract
This paper proposes a novel age estimation method - Global and Local feAture based Age estiMation (GLAAM) - relying on global and local features of facial images. Global features are obtained with Active Appearance Models (AAM). Local features are extracted with regional 2D-DCT (2- dimensional Discrete Cosine Transform) of normalized facial images. GLAAM consists of the following modules: face normalization, global feature extraction with AAM, local feature extraction with 2D-DCT, dimensionality reduction by means of Principal Component Analysis (PCA) and age estimation with multiple linear regression. Experiments have shown that GLAAM outperforms many methods previously applied to the FG-NET database.
Highlights
The wide-ranging topic of facial image (FI) processing has been receiving considerable interest lately because of its real world applications such as forensic art, electronic consumer relationship management, security control and surveillance, cosmetology, entertainment and biometrics
Some issues that should be contemplated are: (i) good discrimination of different people with tolerance to discrepancies inside a class; (ii) face feature recognition (FFR) must be effortlessly performed from raw face images to speedup processing; and (iii) the FFR must lie in a low dimensional space, in order to facilitate the implementation of the classifiers
As FGNET contains face images from 82 subjects, after 82 folds, each subject has been used as test set once, and the final results are calculated based on all estimations
Summary
The wide-ranging topic of facial image (FI) processing has been receiving considerable interest lately because of its real world applications such as forensic art, electronic consumer relationship management, security control and surveillance, cosmetology, entertainment and biometrics. In the FI context, age recognition (or estimation) has been demanding growing attention. Age estimation (AE) can be defined as the process of associating a FI automatically with an exact age or age group. In order to facilitate AE, suitable facial representations are necessary. Even the most robust classifiers will fail due to the inadequacy of the domain where the feature recognition is done [1]. The design of face recognition systems requires careful selection of the face feature recognition (FFR) domain. Some issues that should be contemplated are: (i) good discrimination of different people with tolerance to discrepancies inside a class; (ii) FFR must be effortlessly performed from raw face images to speedup processing; and (iii) the FFR must lie in a low dimensional space, in order to facilitate the implementation of the classifiers
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