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http://dx.doi.org/10.3837/tiis.2020.05.017

Vehicle Face Recognition Algorithm Based on Weighted Nonnegative Matrix Factorization with Double Regularization Terms  

Shi, Chunhe (College of Information Science and Engineering, Northeastern University)
Wu, Chengdong (Faculty of Robot Science and Engineering, Northeastern University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.5, 2020 , pp. 2171-2185 More about this Journal
Abstract
In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.
Keywords
Vehicle face recognition; vehicle matching; Nonnegative Matrix Factorization; classification property; gradient descent method;
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