The research related to age estimation using face images has become increasingly important, due to the fact 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 for defects found in individual global and local features. As for feature classification, the hierarchical classifier, which is composed 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 in the previous works. Consequently, 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, compared to the previous works. 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. Thirdly, 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 showed that the performance of the proposed method was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases. 相似文献
Extensive experimental tests and a computational study of the performance in a cross-flow air classifier have been carried out. A computational fluid dynamics (CFD) package—Fluent—is used to first understand and explain why the cuts or the sharpness of cut of this classifier are not as sharp as they ought to be, and then to optimize the geometry and operational conditions.
Flow fields of the classifier under various set-up conditions and geometry were measured by using laser Doppler anemometry (LDA). Using sieve analyses and the HELOS-laser method, the patterns of behaviour of separation parameters such as cut size and sharpness of cut have been investigated at different boundary conditions.
Using the Fluent package, a two-dimensional computational fluid dynamics model has been developed. The model is based on the Euler–Lagrangian approach. Different turbulence models have been tested. Both Fluent 4.5, with a structured grid, and Fluent 5.1, with structured and unstructured grids, have been used.
Discussions and analyses of the experimental, as well as the computational results, are presented. The simulation with a structured grid shows good agreement with experimental data, except for the sharpness of cut. The reasons of poor performance of the classifier have been found. The geometry is optimized and other conditions were also improved. The performance of the classifier is improved. The experimental observations together with the computed results should increase the depth of understanding of the underlying mechanisms. 相似文献
Based on the features extracted from generalized autoregressive (GAR) model parameters of the received waveform, and the use of multilayer perceptron(MLP) neural network classifier, a new digital modulation recognition method is proposed in this paper. Because of the better noise suppression ability of the GAR model and the powerful pattern classification capacity of the MLP neural network classifier, the new method can significantly improve the recognition performance in lower SNR with better robustness. To assess the performance of the new method, computer simulations are also performed. 相似文献