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Age estimation using a hierarchical classifier based on global and local facial features. (English) Zbl 1209.68596

Summary: The research related to age estimation using face images has become increasingly important, mainly since it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification, both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a combination of global and local features, have received a great deal of attention, because this method can compensate defects found in individual global and local features. As for feature classification, the hierarchical classifier, consisting of an age group classification (e.g. the class of less than 20 years old, the class of 20-39 years old, etc.) and a detailed age estimation (e.g. 17, 23 years old, etc.), provide a much better performance than other methods. However, both the hybrid features and hierarchical classifier methods have only been studied independently and no research combining them has yet been conducted.
We propose a new age estimation method using a hierarchical classifier method based on both global and local facial features. Our research is novel in the following three ways. Firstly, age estimation accuracy is greatly improved through a combination of the proposed hybrid features and the hierarchical classifier. Secondly, new local feature extraction methods are proposed in order to improve the performance of the hybrid features. The wrinkle feature is extracted using a set of region specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin. Finally, the improved hierarchical classifier is based on a support vector machine (SVM) and a support vector regression (SVR). To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier. The experimental results show that the performance of the proposed method is superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.

MSC:

68U10 Computing methodologies for image processing
68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence

Software:

LIBSVM
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