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A weighted version of fusion strategy is introduced for multiple biometrics that compensates the limitations impinged on single biometrics. The least intrusive facial biometrics that combines the facial behaviometrics for person identification is a new trend that needs further investigation. The aim of this study is to apply this weighted fusion scheme for our proposed bi-modal framework that uses...
The information fusion of face and palmprint biometrics using local features is investigated at feature level. The proposed method uses local information extracted from local region of biometric image which has rich statistical information. The texture of each region is processed using multiresolution analysis with different orientations and scales. The feature dimensionality of each region is reduced...
This paper proposes a feature selection method that aims to select an optimal feature subset to representing facial image from the point of view of minimizing the total error rate (TER) of the system. In this proposed approach, the genuine user score distribution and the imposter score distribution are modeled based on a Parzen-window density estimation to enable the direct estimation of total error...
A novel multimodal biometric recognition algorithm based on complex kernel fisher discriminant analysis (complex KFDA) is proposed. Complex KFDA exploits two phases to generalize KFDA and perform classification for the fusion feature set: complex KPCA plus complex LDA. As two distinct biometric modals, the features of iris and face are fused in parallel to test our algorithm. Experimental results...
Ear and face based multimodal recognition could fully utilize their connection relationship, and implement recognizing people without their cooperation because of earpsilas special physiological location and structure. In this paper a kernel-based feature fusion algorithm is presented and applied to multimodal recognition based on fusing ear and face. With the algorithm, the associated feature vectors...
Ear recognition has been proved to be a promising subject in biometrics authentication. The fusion of ear and face biometrics could fully utilize their connection relationship, and possess the merit of recognizing persons without their cooperation because of earpsilas special physiological structure and location. In this paper a feature fusion method based on KPCA is proposed and applied to multimodal...
Feature fusion of palmprint and face based on Kernel Fisher discriminant analysis (KFDA) was proposed in the paper. The essence of KFDA is Kernel Principal Component Analysis (KPCA) plus Linear Discriminant Analysis (LDA).Thus we first obtained the KPCA fusion features, and then calculated the final fusion features by LDA. The discriminant vectors existing in null space and range space of within-class...
Combined with diagonal image transform, two-dimensional discrete cosine transform (2DDCT) is used in face and iris image for feature compression; then Kernel Fisher Discriminant Analysis (KFDA) is chosen as feature fusion; finally, Nearest Neighbor (NN) classifier is selected to perform recognition. Experimental results on ORL (Olivetti Research Laboratory) face database and CASIA (Chinese Academy...
Ear recognition is proved to be a promising authentication technique. Because of earpsilas special physiological structure and location, the fusion of ear and face biometrics could fully utilize their connection relationship of physiological location and the supplement between these two biometrics, and possess the advantage of recognizing people without their cooperation. In this paper a novel feature...
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