This paper introduces a new age estimation method based on the fusion of local features extracted using histogram-based local texture descriptors. In the study the age estimation performances of well-known powerful texture descriptor Local Binary Patterns (LBP), and new texture descriptors Weber Local Descriptor (WLD) and Local Phase Quantization (LPQ) which have not been analyzed in depth for age estimation, are investigated. Also multi-scale and spatial texture analysis is performed for all descriptors. In the spatial texture analysis, a new approach using the Centrally Overlapped Blocks (COB) obtained by combining the centers of discrete blocks is proposed to capture the related information between the blocks. Then feature fusion is performed to investigate the age estimation accuracies of different combinations of local texture descriptors. After dimensionality reduction with Principal Component Analysis (PCA), Multiple Linear Regression (MLR) is used to estimate the specific age. The results show that the age estimation accuracy of the proposed method is better when compared to previous methods on FG-NET, MORPH and PAL databases.