Multispectral palmprint recognition based on three descriptors: LBP, Shift LBP, and Multi Shift LBP with LDA classifier

Local Binary Patterns (LBP) are extensively used to analyze local texture features of an image. Several new extensions to LBP-based texture descriptors have been proposed, focusing on improving noise robustness by using different coding or thresholding schemes. In this paper we propose three algorithms (LBP), Shift Local Binary Pattern (SLBP), and Multi Shift Local Binary Pattern (MSLBP),to extract features for palmprint images that help to obtain the best unique and characteristic values of an image for identification. The Principal Component Analysis (PCA) algorithm has been applied to reduce the size of the extracted feature matrix in random space and in the matching process; the Linear Discriminant Analysis (LDA) algorithm is used. Several experiments were conducted on the large multispectral database (blue, green, red, and infrared) of the University of Hong Kong. As result, distinguished and high results were obtained where it was proved that, the blue spectrum is superior to all spectra perfectly

Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

Local binary pattern (LBP) algorithm and its variants have been used extensively to analyse the local textural features of digital images with great success. Numerous extensions of LBP descriptors have been suggested, focusing on improving their robustness to noise and changes in image conditions. In our research, inspired by the concepts of LBP feature descriptors and a random sampling subspace, we propose an ensemble learning framework, using a variant of LBP constructed from Pascal’s coefficients of n-order and referred to as a multiscale local binary pattern. To address the inherent overfitting problem of linear discriminant analysis, PCA was applied to the training samples. Random sampling was used to generate multiple feature subsets. In addition, in this work, we propose a new feature extraction technique that combines the pyramid histogram of oriented gradients and LBP, where the features are concatenated for use in the classification. Its performance in recognition was evaluated using the Hong Kong Polytechnic University database. Extensive experiments unmistakably show the superiority of the proposed approach compared to state-of-the-art techniques

Multi-scale shift local binary pattern based-descriptor for finger-knuckle-print recognition

Local Binary Pattern (LBP) has been widely used for analyzing local texture features of an image. Several new extensions of LBP based texture descriptors have been proposed, focusing on improving the robustness to noise by using different encoding or thresholding schemes where the most widely known are Median Binary Patterns (MBP), Fuzzy LBP (FLBP), Local Quantized Patterns (LQP), and Shift LBP (SLBP). LBP based descriptors are rarely applied in Finger-Knuckle-Print (FKP) recognition and especially, SLBP-based descriptors has not been reported yet. In this paper we propose using the Multi-scale Shift Binary Pattern (MSLBP) descriptor which extends the original SLBP to multi-scale to get more robust and discriminative representation of FKP features. The classification of this new proposed feature is performed by using Principle Component Analysis and Random subspace Linear Discriminant Analysis and the results suggest that they outperform other classifiers in FKP recognition. Experiments are performed using the PolyU FKP database and the results obtained have shown that the proposed FKP recognition method achieves outstanding rank-1 recognition rate up to 95% compared to the state-of-the-art FKP approaches