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Fusion Based Automatic Human Age Estimation Using Local Descriptors

This paper introduces a new age estimation method based on the fusion of local features extracted using histogram-based local texture descriptors. In this study, the age estimation performances of well-known powerful texture descriptor with improved modification Local Binary Patterns (mLBP), and Local Phase Quantization (LPQ), which have not been analysed in depth for age estimation, investigated. In addition, multi-scale and spatial texture analysis performed for all descriptors. In the spatial texture analysis, a new approach using Overlapped Blocks obtained by discrete blocks proposed to reduce the overlapped age groups between the neighbour age group. Then feature fusion performed to investigate the age estimation accuracies of different combinations of local texture descriptors. Multiple Linear Regression (MLR) used to estimate the specific age at the end of this proposed system. The results show that the age estimation accuracy of the proposed method is better when compared to previous methods on FG-NET, MORPH databases.