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| Abstract: |
| Wavelet decomposition has been applied in palmprint recognition successfully. However, only the low frequency sub-band was used for further feature extraction, while the high frequency sub-bands were considered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. In this paper, we firstly investigate the performances of all the sub-bands by using principal component analysis (PCA) on the BJTU and PolyU palmprint databases, and then use mean filtering to enhance the robustness of the high frequency sub-bands. We find that the preprocessed high frequency sub-bands not only can be used for palmprint recognition but also contain complementary information with the low frequency sub-band. The experimental results show that the performances of the horizontal and vertical high frequency sub-bands can be promoted up to a competitive level, and the fusion scheme, which combines the matching scores of high frequency sub-bands with that of low frequency sub-band, is superior to the conventional recognition methods. |
| Key words: palmprint recognition wavelet transform principal component analysis (PCA) matching score fusion |
| DOI：10.11916/j.issn.1005-9113.2012.06.020 |
| Clc Number:TP391.4 |
| Fund: |
