Palmprint recognition based on weighted adaptive multiple uniform LBP and 2DPCA

• 1、Automation and Electrical Engineering school, Shenyang Ligong University, Shenyang 110159
• 2、Electronic and Information Engineering School, Liaoning Technical University, Huludao 125105

Abstract：In order to solve the problem that the LBP is easily affected by random noise and edge points on the image, and the threshold cannot be automatically selected when the local binary mode describes the texture features of the image, resulting in poor robustness, a palmprint recognition method based on weighted adaptive multiple uniform local binary pattern (WA-MULBP) and two-dimensional principal component analysis 2DPCA is proposed. Firstly, the histogram equalization (HE) is used to perform pretreatment of the palmprint region of interest (ROI) image to reduce the impact of the illumination change during imaging on the final palmprint recognition success rate. Secondly, the preprocessed image is divided into sizes Uniform sub-blocks and use adaptive multiple uniform local binary mode (A-MULBP) algorithm to obtain texture feature histograms and weights of each sub-block. Finally, the texture feature histogram of each sub-block is multiplied and concatenated to obtain the final texture feature histogram, after the 2DPCA dimension reduction, the Euclidean distance discriminant method is used for palmprint recognition. Comparative experiments were conducted on the open standard database of palmprints ,self-built non-contact database and its noise database. The lowest equal error rates are 1.8790% and 4.3803%, 2.0192% and 4.7301%, 2.1849% and 5.050%, 2.6632% and 5.2237%, respectively. Compared with other algorithms, the recognition accuracy and robustness are effectively improved while ensuring real-time performance.

Keywords： Palmprint recognition MULBP Information entropy 2DPCA Robustness