Face recognition based on neuron fuzzy systems
ABSTRACT This paper presents a method for face recognition based on fuzzy clustering and parallel neural networks. Neural networks have been widely used for pattern recognition in various fields. However, the computing efficiency and the recognition rate decrease rapidly if the scale of neural network increases. In this paper, a new method of face recognition based on neuro-fuzzy system is proposed. In particular, the face pattern is divided into several small-scale parallel neural networks based on fuzzy clustering. Experimental results show the effectiveness of the proposed method.
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ABSTRACT: In this paper, we present a method of face recognition using 3D images. We first compensate for the poses of 3D original facial images using feature points and geometrical measurement. Then, the contour maps which are invariant under different illumination conditions are extracted for recognition. In the second step, a method is adopted for face recognition based on fuzzy clustering and parallel neural networks (NN's). Experimental results for 35 persons with different poses and illumination conditions demonstrate the efficiency of our algorithmIEEJ Transactions on Electronics Information and Systems 01/2005; 125(3):426-434.
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ABSTRACT: A neural-network with fuzzified characteristic distances weights (NNFCDW) is proposed in this paper to recognize the facial expressions effectively. During the recognition process, the characteristic distances that represent the relationship between the facial expressions and the muscle movement are used to be the major basis for recognition. The different expressions will somehow dominate the characteristic distances defined from different feature-area (mouth, eye or eyebrow). Therefore, the weights of the characteristic distances will be an important factor to determine the recognition rate. In this paper, a reasonable method of tuning the weights without trial-and-error is proposed. A fuzzy system based on the recognition results is developed to generate the weights rationally. The characteristic distances are multiplied with the fuzzified weights and sent to a neural-network system for recognition of the facial expressions. The proposed neural-network system is composed of the self-organizing map (SOM) neural network and back-propagation neural network (BPNN). When BPNN used the pre-classified data as its training data, the training cycles can be obviously reduced. The experimental results demonstrate that the recognition rate of using the proposed NNFCDW obviously increased about 10% ~ 13% as comparing with the results obtained by using pure BPNN. The computational time of using the proposed NNFCDW is also effectively decreased about 60% as comparing with the results obtained by using pure BPNN.Fuzzy Systems, 2008. FUZZ-IEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on; 07/2008