Abstract
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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