Decision Fusion for Partially Occluded Face Recognition Using Common Vector Approach

  • Mehmet KocEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)


Partial occlusions in the face image negatively affect the performance of a face recognition system. Modular versions of some methods are used to overcome this problem. Modular Common Vector Approach (MCVA) was successfully applied partial occlusion problem. In this work, we apply some well-known decision fusion methods (product rule, borda count, and majority voting) to the decision stage of MCVA approach to increase the classification performance. A well-known appearance based feature descriptor so called Local Binary Patterns (LBP) is used to extract the facial features. The performance comparisons are conducted on AR face database with several experiments. It is observed that combining the classifier outputs using decision fusion methods increase the classification performance of MCVA.


Face recognition Modular Common vector Decision fusion Partial occlusion Local binary patterns 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Bilecik Seyh Edebali UniversityBilecikTurkey

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