Palmprint recognition is an emerging biometric recognition technology. The image vector is processed using principal component analysis, and the vector dimension is generally very high. Two-dimensional principal component analysis directly uses the two-dimensional image matrix to construct the variance matrix. Compared with one-dimensional principal component analysis, it can more accurately calculate the covariance matrix of the original data. Bidirectional two-dimensional principal component analysis is an improved algorithm of two-dimensional principal component analysis. It is applied to palmprint recognition. By performing two-dimensional principal component analysis operations once in the horizontal and vertical directions, the correlation between the rows and columns of the palmprint image is eliminated. The new criteria are used to select the principal components that are more suitable for classification, which greatly compresses the dimension of the feature. The test results of the Palmprint Database of Polytechnic University in Hong Kong show that this method has a higher recognition rate and lower computational complexity.
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