Integration method for face recognition by using sparse representation
A technology of face recognition and sparse representation, which is applied in the field of data classification and face recognition, can solve problems such as unstable results and dictionary differences, and achieve the effects of compensating for instability, making it easy to distinguish, and improving the accuracy rate
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[0020] refer to figure 1 , the specific implementation steps of the present invention are as follows:
[0021] Step 1. Transform all face sample images into a vector, perform normalization and random dimensionality reduction on all vectors, divide the processed vectors into a test sample set and a training sample set at random, and define the test sample set as y , the training sample set is A, where A={A 1 , A 2 , K A N}, N represents the number of categories in the training sample set, A i , i=1, 2, K, N represents the training samples of the i-th class.
[0022] Step 2, use the rotation forest algorithm to generate K rotation matrices, and use the rotation matrix to convert the training sample set A={A 1 , A 2 , K A N} and the test sample set y are mapped to K sets of new training sample sets j=1, 2, K, K and test sample set y j , j=1, 2, K, K.
[0023] Define Y as the label set corresponding to the training sample set A, where Y=[w 1 ,w 2 , K, w N ],w i , i=...
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