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 consid2 ered to b...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 consid2 ered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. In this pa- per, 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 palm- print 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.展开更多
Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information ...Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.展开更多
基金Sponsored by the National Natural Science Foundation of China (Grant No. 60773015)Beijing Natural Science Foundation (Grant No. 4102051)the Fundamental Research Funds for the Central Universities (Grant No. 2009JBZ006)
文摘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 consid2 ered to be unsuitable for palmprint recognition due to their sensitivity to noise and shape distortion. In this pa- per, 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 palm- print 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.
文摘Multimodal biometric fusion is gaining more attention among researchers in recent days. As multimodal biometric system consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a framework for optimal fusion of match scores based on Gaussian Mixture Mode] （GMM） and Monte Carlo sampling based hypothesis testing. The proposed fusion approach has the ability to handle： 1） small size of match scores as is more commonly encountered in biometric fusion, and 2） arbitrary distribution of match scores which is more pronounced when discrete scores and multimodal features are present. The proposed fusion scheme is compared with well established schemes such as Likelihood Ratio （LR） method and weighted SUM rule. Extensive experiments carried out on five different multimodal biometric databases indicate that the proposed fusion scheme achieves higher performance as compared with other contemporary state of art fusion techniques.
