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Control Engineering of China

On Facial Expression Recognition Based on Quantum Neural Networks

Li Peng

Published Jan 1, 2008• 0 citations
OpenAlex

Abstract

The facial expression recognition is an important and complicated problem of pattern recognition field.An approach to facial expression recognition based on multi-level transfer function quantum neural networks(QNN)and multi-layer classifiers is presented in order to improve the recognition rate and recognition reliability.The QNN is trained and tested by the CMU face expression image database.The experiment results show that the method achieves excellent performance in terms of recognition rates and recognition reliability,and the superiority and potential of QNN in solving pattern recognition problems are abvious.

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In this paper, an approach to face recognition is presented, which based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. Firstly, image preprocessing is used to eliminate unrelated information in face images, which could help to locate eyes position. Secondly, feature extraction employs eigenface method based on Karhunen-Loeve transform to extract statistic feature and reduce dimensions. Finally, in the part of face recognition, quantum neural networks based on multi-level transform function are used. The QNN is trained and tested by the ORL faces digital database. At the same time with the classical BP neural network classifiers are compared, the results show that the identification method in more complex environments with a certain degree of robustness and effective and feasible.
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In this paper an improved quantum neural network(QNN) is presented based on multilevel activation function to solve the problems of precision inadequacy and low convergence rate of the BP neural network used in character recognition.Firstly,momentum term is used to updating the weights to accelerate the convergence rate of learning algorithm in QNN.Secondly,a linear superposition of hyperbolic tangent function is used as activation function of hidden unit in the new networks to classify pattern recognition problems that have uncertainty and overlapping data between two patterns.Finally,The experimental results for number,letter,and Chinese character recognition is provided and compared with both BP neural networks and the original Qnn.Results indicate that the improved Qnn not only takes great recognition rate,but also decreases the number of the training time comparing with the original one.The superiority is demonstrated by simulation.