Abstract
This article discusses vision recognition process and finds out that human recognizes objects not by their isolated features, but by their main difference features which people get by contrasting them. According to the resolving character of difference features for vision recognition, the difference feature neural network(DFNN) which is the improved auto-associative neural network is proposed. Using ORL database, the comparative experiment for face recognition with face images and the ones added Gaussian noise is performed, and the result shows that DFNN is better than the auto-associative neural network and it proves DFNN is more efficient.
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Chen, G., Qi, F. Difference feature neural network in recognition of human faces. J. of Electron.(China) 18, 167–173 (2001). https://doi.org/10.1007/s11767-001-0023-6
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DOI: https://doi.org/10.1007/s11767-001-0023-6