Multispectral Palmprint Encoding and Recognition

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.Comment: Preliminary version of this manuscript was published in ICCV 2011. Z. Khan A. Mian and Y. Hu, "Contour Code: Robust and Efficient Multispectral Palmprint Encoding for Human Recognition", International Conference on Computer Vision, 2011. MATLAB Code available: https://sites.google.com/site/zohaibnet/Home/code

The Nonsubsampled Contourlet Transform Based Statistical Medical Image Fusion Using Generalized Gaussian Density

We propose a novel medical image fusion scheme based on the statistical dependencies between coefficients in the nonsubsampled contourlet transform (NSCT) domain, in which the probability density function of the NSCT coefficients is concisely fitted using generalized Gaussian density (GGD), as well as the similarity measurement of two subbands is accurately computed by Jensen-Shannon divergence of two GGDs. To preserve more useful information from source images, the new fusion rules are developed to combine the subbands with the varied frequencies. That is, the low frequency subbands are fused by utilizing two activity measures based on the regional standard deviation and Shannon entropy and the high frequency subbands are merged together via weight maps which are determined by the saliency values of pixels. The experimental results demonstrate that the proposed method significantly outperforms the conventional NSCT based medical image fusion approaches in both visual perception and evaluation indices

A multimodal fusion method for Alzheimer’s disease based on DCT convolutional sparse representation

IntroductionThe medical information contained in magnetic resonance imaging (MRI) and positron emission tomography (PET) has driven the development of intelligent diagnosis of Alzheimer’s disease (AD) and multimodal medical imaging. To solve the problems of severe energy loss, low contrast of fused images and spatial inconsistency in the traditional multimodal medical image fusion methods based on sparse representation. A multimodal fusion algorithm for Alzheimer’ s disease based on the discrete cosine transform (DCT) convolutional sparse representation is proposed.MethodsThe algorithm first performs a multi-scale DCT decomposition of the source medical images and uses the sub-images of different scales as training images, respectively. Different sparse coefficients are obtained by optimally solving the sub-dictionaries at different scales using alternating directional multiplication method (ADMM). Secondly, the coefficients of high-frequency and low-frequency subimages are inverse DCTed using an improved L1 parametric rule combined with improved spatial frequency novel sum-modified SF (NMSF) to obtain the final fused images.Results and discussionThrough extensive experimental results, we show that our proposed method has good performance in contrast enhancement, texture and contour information retention

Image Fusion Algorithm Based on Neighbors and cousins information in Nonsubsampled Contourlet Transform Domain

Nonsubsampled contourlet transform (NSCT) provides flexible multiresolution, anisotropy and directional expansion for images. Compared with the foremost contourlet transform, it is shift-invariant and can overcome the pseudo-Gibbs phenomena around singularities. In addition, coefficients of NSCT are dependent on their neighborhood coefficients in the local window and cousin coefficients in directional subbands. In this paper, region energy and cousin correlation are defined to represent the neighbors and cousins information, respectively. Salience measure, as the combination of region energy and cousin correlation, is defined to obtain fused coefficients in the high-frequency NSCT domain. First, source images are decomposed into subimages via NSCT. Secondly, salience measure is computed. Thirdly, salience measure-maximum-based rule and average rule are employed to obtain high-frequency and low-frequency coefficients, respectively. Finally, fused image is reconstructed by inverse NSCT. Experimental results show that the proposed algorithm outperforms wavelet-based fusion algorithms and contourlet transform-based fusion algorithms.supported by Navigation Science Foundation of China (No.05F07001) and National Natural Science Foundation of China (No.60472081