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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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In order to make the environment of palmprint recognition more flexible and improve the accuracy of touchless palmprint recognition. This paper proposes a robust, touchless, palmprint recognition system which is based on color palmprint images. This system uses skin-color thresholding and hand valley detection algorithm for extracting palmprint. Then, the local binary pattern (LBP) is applied to the palmprint in order to extract the palmprint features. Finally, chi square statistic is used for classification. The experimental results present the equal error rate of 3.7668% and correct recognition rate of 97.0142%. Therefore the results show that this approach is robust and efficient in color palmprint images which are acquired in lighting changes and cluttered background for touch-less palmprint recognition system.
Recently, personalized recommender systems have become indispensable in a wide variety of commercial applications due to the vast amount of overloaded information. Network-based recommendation algorithms for user-object link predictions have achieved significant developments. But most previous researches on network-based algorithm tend to ignore users’ explicit ratings for objects or only select users’ higher ratings which lead to the loss of information and even sparser data. With this understanding, we propose an improved network-based recommendation algorithm. In the process of reallocation of user’s recommendation power, this paper originally transfers users’ explicit scores to users’ interest similarity and user’s representativeness. Finally, we validate the proposed approach by performing large-scale random sub-sampling experiments on a widely used data set (Movielens) and compare our method with two other algorithms by two accuracy criteria. Results show that our approach significantly outperforms other algorithms.
