In this paper a new face recognition method combining independent component analysis (ICA) and BP neural network, named ICABP method, is proposed. Researchers have shown that ICA using higher order statistics is more powerful for face recognition than PCA using up to second order statistics only. However, when the database includes faces with various expressions and different orientations, the superiority of ICA method cannot be shown obviously. In this paper,
the FastICA algorithm is used to extract the independent sources from the face images. Then the conventional minimum Euclidean distance method is replaced by an improved BP neural network with one hidden layer to recognize the faces. The function of local features extraction of ICA and the adaptability of BP neural network are combined perfectly. The experimental results show that our ICABP method is an effective and feasible face recognition method.
In this paper a learning algorithm of synergetic neural network based on selective attention parameters is proposed. According to the mechanism of the Human Visual System (HVS), the weight matrix of synergetic neural network can be obtained by multiplying the prototype matrix by selective attention parameters. Two selective attention models based on the human visual system are put forward in this paper. The comparative experiments between the traditional algorithm SCAP and the new method we proposed in the application of recognizing the real gray images of numeric and alphabetic characters are done. And the results show that our method can improve the synergetic neural network's recognition performance and be more suitable to human visual system.
The binarization of license plate image is one of the key techniques of car license plate recognition (CLPR) system and its results influence the accuracy of the segmentation of characters and their identification directly. In this paper, by analyzing the limitations of Otsu's method and Bernsen's method, a practical method of license plate binarization based on histogram analysis is proposed. In this method, the feature that the percentage of the character area is always less than that of background is presented to distinguish the style of plate. Then a global thresholding method, Doyle's method, is used to threshold the plate image. By counting over 8,000 pieces of plate images, the accuracy is nearly 99%. Only those pictures which are badly polluted or with very low resolution cannot be binarized correctly. The experimental and field-tested results show that our method has higher accuracy, higher speed and better binarization effect. The method has been applied in our CLPR system successfully.
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