Identification and Verification using FINGERPRINT has direct relation with quality of FINGERPRINT. In this paper، novel method is proposed to enhance the quality of FINGERPRINT using Arc-Gabor filter bank. Indeed proposed filter bank is an adaptive version of standard filter bank for FINGERPRINT. Evaluating the ability of proposed method is done using two methods. In the first method، results of verification and identification using normalized histogram of Binarized Statistical Image Feature (BSIF) are compared for the FINGERPRINTs before and after using proposed method. And in the second method، Peak Signal to Noise Ratio (PSNR) is used. Two databases DBI and DBII are used for evaluation. Using proposed method، for verification Equal Error Rate، are decrease from (15. 89% and 11. 70%) to (11. 35% and 8. 00%)، respectively، and for identification، 1st rank correct recognition rate are increased from (69. 28% and 71. 16%) to (78. 80% and 81. 70%)، respectively. Average values of PSNR for enhanced FINGERPRINT using proposed methods are more than other version of Gabor filter.

FINGER-Knuckle-PRINT (FKP) is an accurate and reliable biometric in compare to other hand-based biometrics like FINGERPRINT because of the FINGER's dorsal region is not exposed to surfaces. In this paper, a simple end-to-end method based on Convolutional Neural Network (CNN) is proposed for FKP recognition. The proposed model is composed only of three convolutional layers and two fully connected layers. The number of trainable parameters hereby has significantly reduced. Additionally, a straightforward method is utilized for data augmentation in this paper. The performance of the proposed network is evaluated on Poly-U FKP dataset based on 10-fold cross-validation. The best recognition accuracy, mean accuracy and standard deviation are 99.83%, 99.18%, and 0.76, respectively. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of recognition accuracy and the number of trainable parameters. Also, in compare to four fine-tuned CNN models including AlexNet, VGG16, ResNet34, and GoogleNet, the proposed simple method achieved higher performance in terms of recognition accuracy and the numbers of trainable parameters and training time.

The content based image retrieval searches digital images in a large image database and uses visual content of images instead of metadata. This approach has many usages in security and authentication for example scanning the iris, FINGERPRINT or FINGER knuckle PRINT. This paper contributes a new method for personal authentication using FINGER knuckle PRINT based on a new local binary pattern and image segmentation. The capabilities of quantum science lead to take its advantage in different areas of image processing. The main idea is inspired by the theory of quantum estimation and is applied to the feature extraction phase, in addition, the quantum circuit of the proposed feature is also designed. In order to measure the efficiency and accuracy of the proposed method, the EER (Equal Error Rate) is calculated. After implementing the proposed algorithm on the POLYU dataset, which contains 7920 images, the EER = 0. 67 and accuracy =99% are obtained which indicate that the new method has more efficiency and accuracy than the similar approaches.

In general, identification and verification are done by passwords, pin number, etc., which are easily cracked by others. To overcome this issue, biometrics has been introduced as a unique tool to authenticate an individual person. Biometric is a quantity which consists of individual physical characteristics that provide more authentication and security than the password, pin number, etc. Nevertheless, unimodal biometric suffers from noise, intra class variations, spoof attacks, non-universality and some other attacks. In order to avoid these attacks, the multimodal biometrics, i.e. a combination of more modalities is adapted. Hence this paper has focused on the integration of FINGERPRINT and FINGER Knuckle PRINT (FKP) with feature level fusion. The features of FINGERPRINT and (FKP) are extracted. The feature values of FINGERPRINT using Discrete Wavelet Transform and the key points of FKP are clustered using K-Means clustering algorithm and their values are fused. The fused values of K-Means clustering algorithm is stored in a database which is compared with the query FINGERPRINT and FKP K-Means centroid fused values to prove the recognition and authentication. The comparison is based on the XOR operation. Hence this paper provides a multimodal biometric recognition method to provide authentication with feature level fusion. Results are performed on the PolyU FKP database and FVC 2004 FINGERPRINT database to check the Genuine Acceptance Rate (GAR) of the proposed multimodal biometric recognition method. The proposed multimodal biometric system provides authentication and security using K-Means clustering algorithm with GAR=99.4%, FRR=0.6% and FAR=0% with security of 128 bits for each modality.

In this paper a new texture descriptor is presented for biometric authentication systems based on hand dorsal texture characteristics. First, different hand images are collected using an acquisition device, then the images are mapped from RGB color space to YUV and the five FINGERs are isolated from images with thresholding of the U component for skin detection. Utilizing the center of gravity, length and direction of the FINGERs, the FINGER knuckle PRINT and nail regions are determined. Then, the texture features of FINGER knuckle are described using four directional patterns based on statistical thresholding. Moreover, the features of nails are described using discrete wavelet transform. In addition, utilizing a cost function in the evolutionary particle swarm optimization algorithm, a set of significance coefficients is applied to the textural features of the hand, and as a result, these features are optimized. Finally, using the d1 distance measurement criterion, the similarity of the images has been calculated and the identity of individuals has been identified with the recognition rate of 92.13%. The experimental results on a set of images collected by the authors, called FKP_Nail, show that the proposed method is more accurate and faster than known existing methods while it uses simpler calculations.

Background & Aim: Conceding the wide effect of high blood pressure on different organs of body & its different complications and as this disease doesn't have any signs and people don't aware about their dieses when they are affected by FINGER PRINT & modifying the association between that &FINGER PRINT, it is possible to diagnose the disease on them & take the right treatments.Materials & Methods: In this research which was case-control research we collected FINGERPRINTs from 100 persons that were at Ali-Ebn-Abitaleb Hospital and 100 normal persons that the same paper is used and characteristic in two group classified in 3 categories: whirl Arch loop and from the SPSS and t-test method was made for statistic analysis.Results: Results showed from 2000 samples of FINGER PRINT that were taken from 100 persons that had blood pressure and 100 persons that didn't have blood pressure, the average loop FINGERPRINT in blood pressure group was 3.82±2.27 and in non blood pressure group 6.5±1.81 that has the significance deterrence (P value=0.023).The average Whirl FINGERPRINT in blood pressure group was 4.73±2.59 and in none blood pressure group was 2.29±1.93 that has the significances difference (P value= 0.002) and the average arch FINGER PRINT in blood pressure group was 1.43±1.55 and in none blood pressure group was 1.04+0.93 that has the significance difference. (P value=0.0001). Conclusion: Among FINGERPRINTs and high blood pressure are relationships and on the basis of the resolutions of research possibility of taking to high blood pressure in people that have whirl FINGERPRINT is more.

Background: Rheumatoid arthritis (RA) is a chronic, systematic inflammatory disorder that may affect many tissues and organs, but principally attacks synovial joints and it is a common rheumatic disease with many subtypes. Nuclear Magnetic resonance (1H NMR) spectrometers with high sensitivity, resolution and dynamic range has permitted the rapid, simultaneous investigation of complex mixtures of endogenous or exogenous components present in biological materials. Metabonomics is the systematic study of chemical FINGER PRINT resulted from cell reactions and could be used as a new biomarker for early disease diagnosis. In the present investigation, we studied serum METABOLIC profile in rheumatoid arthritis (RA) in order to find out the METABOLIC FINGER PRINT pattern of the disease.Materials and methods: In our metabonomics study serum samples were collected from 16 patients with active RA, and from equal number of healthy subjects. They were evaluated during a one-year follow-up with the assessment of disease activity and 1H NMR spectroscopy of sera samples. In all the cases, the presence of active rheumatoid arthritis was shown by an increase in the T1 values of the synovium of the joints. We specified and classified all metabolites using PCA, PLSDA chemometrics methods. Chenomx (Trail Version) and ProMetab codes in Matlab software environments were used for our data analysis. Results were compared with the NMR metabolite data bank (www.metabolomics.ca). Anti-CCP, ANA and urea were also analyzed by ElISA and colorimetric methods respectively.Results: The most changes identified in this study were in the biosynthesis pathways of steroid hormones, biotin, fatty acids, amino acids (Leucine, Valin and isoleucine) and also linoleic acid.Conclusion: In rheumatoid arthritis disease, the activation of the immune system consumes larg amounts of energy. The main donor of free energy in cells is ATP, which is generated by both glycolysis and oxidative phosphorylation. Changes in amino acids and free fatty acids biosynthesis pathways confirm the high energy utilization. In this disease, the increase in free fatty acid metabolism leads to production of Acetyl CoA and ketone bodies. Since there are many diseases subtype in rheumatoid arthritis, more sensitive diagnostic method is required. The result of our investigation suggests that metabolome profiling method could be used as a new biomarker for early diagnosis of rheumatoid arthritis disease.

METABOLIC syndrome (MetS) is characterized by a cluster of cardiovascular risk factors that include: abdominal obesity, dyslipidaemia, hypertension, insulin resistance and impaired glucose tolerance. Recent genome wide association studies have identified several susceptibility regions involved in lipid metabolism that are also associated with MetS. We have explored the association of 9 genetic polymorphisms involved in lipid metabolism and hypertension, including: MTHFR C677T, SELE L554F, FGB - 455G>A, GNB3 C825T, ZNF259 C>G, PSRC-1A>G, CETP I405V, LPL S447X and LPA C>T in 97 subjects with MetS and 96 individuals without MetS who were recruited randomly from Mashhad stroke and heart atherosclerotic disorder (MASHAD) study using a stratified cluster random sampling technique. Anthropometric parameters and biochemical measurements were determined in all the subjects. Genotyping was carried out followed by univariate and multivariate analyses. The subjects with MetS had a higher triglyceride and lower HDL- C. CG+GG genotypes ofZNF259 polymorphism (rs964184 C>G) and TT+CT genotypes of MTHFR C677T (rs1801133) were associated with MetS, and individuals carrying the G allele forZNF259 or the T allele for MTHFR polymorphisms were associated with MetS (e.g, odds ratio (OR) for CG+GG genotypes vs. CC wild type: 2.52, CI=1.33-4.77; P=0.005). However, after multiple comparison adjustment, this relationship remained significant only for CG+GG genotypes ofZNF259 polymorphism. Moreover, the ZNF259 CG+GG genotypes were associated with increased serum concentrations of triglycerides and LDL-C, compared to the wild type. These data support the necessity for further studies in larger multicenter settings.