A novel age estimation method is presented which improves performance by fusing complementary information acquired from global and local features of the face. Two-directional two-dimensional principal component analysis ((2D)2PCA) is used for dimensionality reduction and construction of individual feature spaces. Each feature space contributes a confidence value which is calculated by Support vector machines (SVMs). The confidence values of all the facial features are then fused for final age estimation. Experimental results demonstrate that fusing multiple facial features can achieve significant accuracy gains over any single feature. Finally, we propose a fusion method that further improves accuracy.

A wide variety of visual textures could be successfully modeled as spatially variant by quantitatively describing them through the variation of their local spatial frequency and/or local orientation components. This class of patterns includes flow-like, granular or oriented textures. Modeling is achieved by assuming that locally, textured images contain a single dominant component describing their local spatial frequency and modulating amplitude or contrast. Spatially variant textures are non-homogeneous in the sense of having nonstationary local spectra, while remaining locally coherent. Segmenting spatially variant textures is the challenging task undertaken in this paper. Usually, the goal of texture segmentation is to split an image into regions with homogeneous textural properties. However, in the case of image regions with spatially variant textures, there is no global homogeneity present and thus segmentation passes through identification of regions with globally nonstationary, but locally coherent, textural content. Local spatial frequency components are accurately estimated using Gabor wavelet outputs along with the absolute magnitude of the convolution of the input image with the first derivatives of the underlying Gabor function. In this paper, a frequency estimation approach is used for segmentation. Indeed, at the boundary between adjacent textures, discontinuities occur in texture local spatial frequency components. These discontinuities are interpreted as corresponding to texture boundaries. Experimental results are in remarkable agreement with human visual perception, and demonstrate the effectiveness of the proposed technique.