This paper proposes a pose estimation and frontal face detection
algorithm for face recognition. Considering it's application in a
real-world environment, the algorithm has to be robust yet
computationally efficient. The main contribution of this paper is
the efficient face localization, scale and pose estimation using
color models. Simulation results showed very low computational
load when compare to other face detection algorithm. The second
contribution is the introduction of low dimensional statistical
face geometrical model. Compared to other statistical face model
the proposed method models the face geometry efficiently. The
algorithm is demonstrated on a real-time system. The simulation
results indicate that the proposed algorithm is computationally
efficient.
In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial membership functions, or initial weights. Fuzzy IF-THEN rules are constantly combined and pruned to minimize the size of the network while maintaining accuracy; irrelevant inputs are detected and deleted, and membership functions and network weights are trained with a gradient descent algorithm, i.e., error backpropagation. Experimental studies on synthesized data sets demonstrate that the proposed Fuzzy Neural Network is able to achieve accuracy comparable to or higher than both a feedforward crisp neural network, i.e., NeuroRule, and a decision tree, i.e., C4.5, with more compact rule bases for most of the data sets used in our experiments. The FNN has achieved outstanding results for cancer classification based on microarray data. The excellent classification result for Small Round Blue Cell Tumors (SRBCTs) data set is shown. Compared with other published methods, we have used a much fewer number of genes for perfect classification, which will help researchers directly focus their attention on some specific genes and may lead to discovery of deep reasons of the development of cancers and discovery of drugs.
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