Conventional representation methods try to express the test sample as a weighting sum of training samples and exploit the deviation between the test sample and the weighting sum of the training samples from each class (also referred to as deviation between the test sample and each class) to classify the test sample. In particular, the methods assign the test sample to the class that has the smallest deviation among all the classes. This paper analyzes the relationship between face images under different poses and, for the first time, devises a bidirectional representation method-based pattern classification (BRBPC) method for face recognition across pose. BRBPC includes the following three steps: the first step uses the procedure of conventional representation methods to express the test sample and calculates the deviation between the test sample and each class. The second step first expresses the training sample of a class as a weighting sum of the test sample and the training samples from all the other classes and then obtains the corresponding deviation (referred to as complementary deviation). The third step uses the score-level fusion to integrate the scores, that is, deviations generated from the first and second steps for final classification. The experimental results show that BRBPC classifies more accurately than conventional representation methods.
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