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.
A subspace projection captures the global characteristics of a palm by projecting to the most varying (in case of PCA) or the most discriminative (in case of LDA) dimensions. Subspace projection methods include eigenpalm @cite_33 , which globally projects palm images to a PCA space, or fisherpalm wu2003fisherpalms which projects to an LDA space. However, the finer local details are not well preserved and modeled by such subspace projections. @cite_17 fused palmprint and palmvein images and proposed the representation. Unlike the eigenpalm @cite_33 or the fisherpalm wu2003fisherpalms , the representation attempts to preserve the local characteristics as well while projecting onto a subspace. @cite_37 represented multispectral palmprint images as quaternion and applied quaternion PCA to extract features. A nearest neighbor classifier was used for recognition using quaternion vectors. The quaternion model did not prove useful for representing multispectral palm images and demonstrated low recognition accuracy compared to the state-of-the-art techniques. The main reason is that subspaces learned from misaligned palms are unlikely to generate accurate representation of each identity.
@cite_37 @cite_33 @cite_17
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Abstract-Palmprint has been widely used in personal recognition. To improve the performance of the existing palmprint recognition system, multispectral palmprint recognition system has been proposed and designed. This paper presents a method of representing the multispectral palmprint images by quaternion and extracting features using the quaternion principal components analysis (QPCA) to achieve better performance in recognition. A data acquisition device is employed to capture the palmprint images under Red, Green, Blue and near-infrared (NIR) illuminations in less than 1s. QPCA is used to extract features of multispectral palmprint images. The dissimilarity between two palmprint images is measured by the Euclidean distance. The experiment shows that a higher recognition rate can be achieved when we use QPCA. Given 3000 testing samples from 500 palms, the best GAR is 98.13 . In this paper, we propose a palmprint recognition method based on eigenspace technology. By means of the Karhunen-Loeve transform, the original palmprint images are transformed into a small set of feature space, called "eigenpalms", which are the eigenvectors of the training set and can represent the principle components of the palmprints quite well. Then, the eigenpalm features are extracted by projecting a new palmprint image into the subspace spanned by the "eigenpalms", and applied to palmprint recognition with a Euclidean distance classifier. Experimental results illustrate the effectiveness of our method in terms of the recognition rate. Unimodal analysis of palmprint and palm vein has been investigated for person recognition. However, they are not robust to noise and spoof attacks. In this paper, we present a multimodal personal identification system using palmprint and palm vein images with fusion applied at the image level. The palmprint and palm vein images are fused by a novel integrated line-preserving and contrast-enhancing fusion method. Based on our proposed fusion rule, the modified multiscale edges of palmprint and palm vein images are combined as well as the image contrast and the interaction points (IPs) of the palmprints and vein lines are enhanced. The IPs are novel features obtained in our fused images. A novel palm representation, called "Laplacianpalm" feature, is extracted from the fused images by Locality Preserving Projections (LPP). We compare the recognition performance using the unimodal and the proposed fused images. We also compared the proposed "Laplacianpalm " approach with the Fisherpalm and Eigenpalm on a large dataset. Experimental results show that the proposed multimodal approach provides a better representation and achieves lower error rates in palm recognition.