Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003 on PolyU and 0.2 on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.
Fusion of spectral bands has been demonstrated at feature level. @cite_22 used feature level band fusion for multispectral palmprints. Specifically, a modification of CompCode was combined with the original CompCode and features from the pair of less correlated bands were fused. The results indicated an improvement over image level fusion and were comparable to match-score level fusion. Zhou and Kumar @cite_11 encoded palm vein features by enhancement of vascular patterns and using the Hessian phase information. They showed that a combination of various feature representations can be used for achieving improved performance based on palmvein images. @cite_14 investigated fuzzy and sigmoid features for multispectral palmprints and a rank-level fusion of scores using various strategies. It was observed that a nonlinear fusion function at rank-level was effective for improved recognition performance. @cite_36 used gabor kernels for feature extraction from multispectral palmprints and a rank-level fusion scheme for fusing the outputs from individual band comparisons. One drawback of rank-level fusion is that it assigns fixed weights to the rank outputs of spectral bands, which results in sub-optimal performance.
@cite_36 @cite_14 @cite_22 @cite_11
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Multispectral imaging has been effectively used in the fields of computer vision and human verification to analyze information captured from several bands of the electromagnetic spectrum recently. In this paper, a novel Gabor-based palm print identification algorithm on multispectral images is proposed. Various features are extracted from convolving the Gabor kernels with each image sample. Logistic regression method is used to fuse obtained ranks of each spectral band authentication. The proposed algorithm is evaluated on PolyU database which contains palm print images from 400 individuals from each band. The obtained results show robustness of our algorithm in comparison with other works that presented in the literature. This paper presents an approach for the personal authentication using rank-level fusion of multispectral palmprints, instead of using multiple biometric modalities and multiple matchers. The rank level fusion involving the non linear combination of hyperbolic tangent functions gives the best recognition rate for the Rank 1 obtained from two types of features, viz., sigmoid and fuzzy. The results of using rank level fusion on the publicly available multispectral palmprint database show the significant improvement in the recognition rate as compared to the individual spectral bands. Recognition rate of 99.4 from sigmoid features and that of 99.2 from fuzzy features based on Rank 1 is the outcome of the hyperbolic tangent nonlinearity. Palmprint is a reliable and accurate trait in the biometric family. To improve the accuracy of the existing palmprint systems, multispectral imaging is used for palmprint recognition. Although image level fusion and matching score level fusion were widely used for multispectral palmprint recognition, feature level fusion was paid less attention. This paper proposes a novel method for multispectral palmprint recognition by the feature level fusion technique. Experimental results on a large public multispectral database show that the proposed method could get better results than image level fusion, and it could get comparable results with matching score level fusion with less cost of storage. This paper investigates some promising approaches for the automated personal identification using contactless palmvein imaging. We firstly present two new palmvein representations, using Hessian phase information from the enhanced vascular patterns in the normalized images and secondly from the orientation encoding of palmvein line-like patterns using localized Radon transform. The comparison and combination of these two palmvein feature representations, along with others in the palmvein literature, is presented for the contactless palmvein identification. We also evaluate the performance from various palmvein representations when the numbers of training samples are varied from minimum. Our experimental results suggest that the proposed representation using localized Radon transform achieves better or similar performance than other alternatives while offering significant computational advantage for online applications. The proposed approach is rigorously evaluated on the CASIA database (100 subjects) and achieves the best equal error rate of 0.28 . Finally, we propose a score level combination strategy to combine the multiple palmvein representations. We achieve consistent improvement in the performance, both from the authentication and recognition experiments, which illustrates the robustness of the proposed schemes.