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Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

Intensity inhomogeneity often occurs in real-world images, and inevitably leads to many difficulties for accurate image segmentation. Although a lot of level set methods have been proposed to solve the problem of intensity inhomogeneity, they are often unavailable for some images with severe intensity inhomogeneity. In this paper, we propose a novel level set based segmentation model to effectively segment those images with severe intensity inhomogeneity, which is named as Local Salient Fitting (LSF) model. In LSF, we firstly transform original image into the new modality in which the object and background regions can be discriminated easily. Specially, we propose a weight factor based on local intensity variation to highlight the local region contrast of image. Meanwhile, the variation degree of local region is also computed to extract the distribution information of intensity variation. Then, since the new modality embodies the distribution information of intensity variation, we utilize the idea of fitting region distribution information to construct the salient fitting term. Finally, the salient fitting term is incorporated into the level set method to segment intensity inhomogeneous images. Furthermore, the combined effect of highlighted local region contrast and statistical distribution information of intensity variation will enhance the robustness of our method. Experiments conducted on synthetic and real images clearly demonstrate the efficiency and robustness of the proposed LSF model.

Local binary patterns (LBP) are one of the most important image representations. However, LBPs have not been as successful as other methods in palmprint recognition. Originally, the LBP descriptor methods construct feature vectors in the image intensity space, using pixel intensity differences to encode a local representation of the image. Recently, similar feature descriptors have been proposed which operate in the gradient space instead of the image intensity space, such as local directional patterns (LDP) and local directional number patterns (LDN). In this paper, we propose a new feature input space and define an LBP-like descriptor that operates in the local line-geometry space, thus proposing a new image descriptor, local line directional patterns (LLDP). Moreover, the purpose of this work is to show that different implementations of LLDP descriptors perform competitively in palmprint recognition. We evaluate variations to LLDPs, e.g., the modified finite radon transform (MFRAT) and the real part of Gabor filters are exploited to extract robust directional palmprint features. As is well-known, palm lines are the essential features of a palmprint. We are able to show that the proposed LLDP descriptors are suitable for robust palmprint recognition. Finally, we present a thorough performance comparison among different LBP-like and LLDP image descriptors. Based on experimental results, the proposed feature encoding of LLDPs using directional indexing can achieve better recognition performance than that of bit strings in the Gabor-based implementation of LLDPs. We used four databases for performance comparisons: the Hong Kong Polytechnic University Palmprint Database II, the blue band of the Hong Kong Polytechnic University Multispectral Palmprint Database, the Cross-Sensor palmprint database, and the IIT Delhi touchless palmprint database. Overall, LLDP descriptors achieve a performance that is competitive or better than other LBP descriptors. We propose LLDP descriptor, which uses the line feature to calculate the code.We show that line direction index number is better than bit strings for coding.LLDP achieves the best recognition performance among all LBP-structure descriptors.LLDP achieves promising recognition performance on four palmprint databases.

In this paper, a novel level set segmentation model integrating the intensity and texture terms is proposed to segment complicated two-phase nature images. Firstly, an intensity term based on the global division algorithm is proposed, which can better capture intensity information of image than the Chan-Vese model (CV). Particularly, the CV model is a special case of the proposed intensity term under a certain condition. Secondly, a texture term based on the adaptive scale local variation degree (ASLVD) algorithm is proposed. The ASLVD algorithm adaptively incorporates the amplitude and frequency components of local intensity variation, thus, it can extract the non-stationary texture feature accurately. Finally, the intensity term and the texture term are jointly incorporated into level set and used to construct effective image segmentation model named as the Intensity-Texture model. Since the intensity term and the texture term are complementary for image segmentation, the Intensity-Texture model has strong ability to accurately segment those complicated two-phase nature images. Experimental results demonstrate the effectiveness of the proposed Intensity-Texture model. An intensity term based on the so-called global division algorithm is proposed.We extract the amplitude and frequency components of local intensity variation.We propose the adaptive scale local variation degree algorithm as texture term.The intensity and texture terms are integrated into level set energy functional.

Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilize all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain. In this paper, we propose a general framework for DR-based method named complete DR (CDR), which reveals DR by a comprehensive and complete way. Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning. This way, CDR subsumes previous methods as special cases. Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance. Motived this way, we propose a novel palmprint recognition algorithm in frequency domain. First, we extract CDR using multi-scale modified finite radon transformation. Then, an effective correlation filter, namely, band-limited phase-only correlation, is explored for pattern matching. To remove feature redundancy, the sequential forward selection method is used to select a small number of CDR images. Finally, the matching scores obtained from different selected features are integrated using score-level-fusion. Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications.

In computer vision and pattern recognition, handcrafted local features play an important role in many tasks. Many effective handcrafted local features have been proposed. Among them, Weber Local Descriptor (WLD) is a successful one. WLD is a simple but powerful descriptor, and a lot of variants of WLD have also been proposed in recent years, which has been broadly used for texture classification as well as biometrics. In this paper, we make a review for WLD and its variants. Generally, the algorithms of WLD and its variants can be divided into categories such as differential excitation-based, orientation-based and multiple features based. We also summarize their applications for biometrics.

As one of new-emerging biometrics techniques, palm vein recognition has received wide attentions recently. In recent years, local descriptor, direction coding and correlation filters-based methods are popular for palmprint, palm vein, and finger vein recognition. In this paper, we make a performance evaluation for palm vein recognition using these methods. The experimental results show that the methods based on direction information can achieve better recognition performance.

Neurodegenerative diseases and neural injury are 2 of the most feared disorders that afflict humankind by leading to permanent paralysis and loss of sensation. Cell based treatment for these diseases had gained special interest in recent years. Previous studies showed that dental pulp stem cells (DPSCs) could differentiate toward functionally active neurons both in vitro and in vivo, and could promote neuranagenesis through both cell-autonomous and paracrine neuroregenerative activities. Some of these neuroregenerative activities were unique to tooth-derived stem cells and superior to bone marrow stromal cells. However, DPSCs used in most of these studies were mixed and unfractionated dental pulp cells that contain several types of cells, and most were fibroblast cells while just contain a small portion of DPSCs. Thus, there might be weaker ability of neuranagenesis and more side effects from the fibroblast cells that cannot differentiate into neural cells. p75 neurotrophin receptor (p75NTR) positive DPSCs subpopulation was derived from migrating cranial neural crest cells and had been isolated from DPSCs, which had capacity of differentiation into neurons and repairing neural system. In this article, we hypothesize that p75NTR positive DPSCs simultaneously have greater propensity for neuronal differentiation and fewer side effects from fibroblast, and in vivo transptantation of autologous p75NTR positive DPSCs is a novel method for neuranagenesis. This will bring great hope to patients with neurodegenerative disease and neural injury.

The first branchial arch malformation (FBAM) is a rare congenital defect associated with anomalous development of the first and second branchial arches. Cause of FBAM still remains unknown, and is thought in most cases to be multifactorial, involving both genetic and enviromental factors. Dlx2 as a member of the Dlx homeobox gene family, plays a crucial role in the development of the first branchial arch. The tissues regulated mainly by Dlx2 are coincident with the tissues mainly involved in FBAM. Dlx2 over-expression generated by electroporation transfection can disturb the migration and differentiation of cranial neural crest cells (CNCCs), which migrate to the branchial arches and in turn give rise to much of the facial skeleton and connective tissues. Furthermore, Dlx2 over-expression can be found in the first branchial arch spontaneous mutant mice. So we hypothesize that Dlx2 over-expression mutation causes FBAM due to an increase in cell-cell adhesion and inhibiting the migration of CNCC to the first branchial arch in the early stage, or migrating to an incorrect position and can't differentiate into normal tissues. What an exact role of Dlx2 over-expression in FBAM remains to be investigated and Dlx2 over-expression transgenic mouse will be a nice model for further research in FBAM.

J Oral Pathol Med (2010) 39 182–187 Background: Candida albicans is the important opportunistic fungal pathogens which can cause oral Candidiasis and even more seriously systemic infection. Apoptosis of C. albicans induced by environmental factor such as weak acid and antifungal drugs were studied recently. Illustrating the phenomenon of apoptosis in C. albicans may help us to discover new antifungal therapy by activating the fungal cells to suicide. Methods: Two oral C. albians clinical isolates which isolated respectively from healthy host [Strain 23C: minimal inhibition concentration (MIC) is 0.125 μg/ml for Amphotericin B (AmB)] and advanced cancer patient (Strain 28A: MIC is 2 μg/ml for AmB), were induced by 1 μg/ml AmB in vitro for 200 min, and then studied the apoptosis markers using terminal deoxynucletidyltransferase-mediated dUTP nick end labeling (TUNEL) (shown by diaminobenzidine and fluorescent isothiocyanate), and the ultrastructure of cell nuclear using transmission electron microscope (TEM), quantitative analysis using flow cytometry for the rapid exposure of phosphatidylserine at the outer membrane and propodium iodide (PI) double staining. C. albicans conference strain YEM30 was used as the control strain. Results: With TUNEL assay and TEM, we detected the typical characteristics of apoptosis. Strain 23C (with low MIC) showed significantly higher percentage of apoptosis (19.92%) compared with Strain 28A (with high MIC) which was isolated from the cancer patient (7.29%) (P

Candida albicans is one of the main opportunistic pathogen for human , the aim of this study is to investigate the phenomena of apoptosis in oral Candida albicans induced by acetic acid.The Candida albicans of clinical strains were induced to apoptosis by using a weak acid acetic acid.The apoptosis was detected by flow cytometry and TEM. The data were processed for Chi-square test using SPSS11.5 software package.Oral Candida albicans had classic apoptosis when induced by proper concentration of acetic acid, and different concentrations of acetic acid had variable ability of inducing apoptosis of Candida albicans.Apoptosis can be detected in clinical strains of Candida albicans, the mechanism of apoptosis needs further research for the purpose of developing new antifungal drugs. Supported by National Natural Science Foundation of China(Grant No.30400498) and 2007 National College Student Innovative Planning Project.