College of Electronic Information Engineering, Zhengzhou SIAS University, Zhengzhou 451150, China.

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Abstract—Palmprint based authentication has been investigated over 20 years, and many different problems related to palmprint recognition have been addressed, but it is still a challenging topic due to its importance, superiority, convenience and palmprints are often influenced by a lot of factors, such as illumination, viewing angle, noise, intentional fraud, wear, imperfection and pollution. Local discriminant canonical correlation analysis (LDCCA) is a well-known dimensional reduction algorithm to extract valuable information from multi-kinds of feature sets. Based on LDCCA, an authentication recognition method is proposed by using left and right palmprint. The method considers a combination of local properties and discrimination between different classes, including not only the correlations between sample pairs but also the correlations between samples and their local neighborhoods. Effective class separation is achieved by maximizing local within-class correlations and minimizing local between-class correlations simultaneously. The experimental results on a public multimodal palmprint database CASIA validate the effectiveness of the proposed methods.

Index Terms—Palmprint recognition, palmprint pair identification, Canonical Correlation Analysis (CCA), Local Discriminant CCA (LDCCA).

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Cite: Shanwen Zhang, Qinging Zhang, Hao Yuan, "Palmprint Identification Integrating Left and Right by Local Discriminant Canonical Correlation Analysis," Journal of Computers vol. 14, no. 9, pp. 580-589, 2019.