Abstract A classification algorithm based on multi-dimensional similarity distribution is presented to enhance the accuracy in open set face recognition. This algorithm firstly get the similarity vector distribution of known and unknown samples by testing on many labeled pictures. Then those similarity vectors are learned by linear discriminant analysis (LDA) to extract distribution features. Finally, the proposed algorithm rejects the unknown identity by feature-matching. Hence, the feature has strong classification ability in view of the discrimination information abstracted from the similarity distribution. Experimental results on several face databases demonstrate that the proposed method significantly outperforms the traditional method for open set face recognition.
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2011, Vol. 24 
