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

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications

Finger Vein Recognition Based on PCA Feature using Artificial Neural Network

Personal recognition technology is developing rapidly as a security system. Traditional methods such as authentication key; password: card is not secure enough, because they could be stolen or easily forget. Biometrics has been applied to a wide range of systems. According to various researchers, vein biometrics was a good technique from other biometric authentication system used, such as fingerprints, hand geometry, voice, etc. of the DNA. Root Authentication systems can be designed in different ways. All methods include the matching stage. A neural network is an effective way of matching Personal identification authentication system. The finger vein pattern is unique biometric identity of the human beings. The finger vein recognition is a popular biometric technique which is used for authentication purposes in various applications. In the propose work an algorithm is proposed to find the accuracy, FRR and FAR of finger vein recognition. The performances of PCA, threshold segmentation, pre-processing and testing & training techniques has been validate and compared with each other in order to determine the most accurate results in terms of finger vein recognition

Achieving Information Security by multi-Modal Iris-Retina Biometric Approach Using Improved Mask R-CNN

The need for reliable user recognition (identification/authentication) techniques has grown in response to heightened security concerns and accelerated advances in networking, communication, and mobility. Biometrics, defined as the science of recognizing an individual based on his or her physical or behavioral characteristics, is gaining recognition as a method for determining an individual\u27s identity. Various commercial, civilian, and forensic applications now use biometric systems to establish identity. The purpose of this paper is to design an efficient multimodal biometric system based on iris and retinal features to assure accurate human recognition and improve the accuracy of recognition using deep learning techniques. Deep learning models were tested using retinographies and iris images acquired from the MESSIDOR and CASIA-IrisV1 databases for the same person. The Iris region was segmented from the image using the custom Mask R-CNN method, and the unique blood vessels were segmented from retinal images of the same person using principal curvature. Then, in order to aid precise recognition, they optimally extract significant information from the segmented images of the iris and retina. The suggested model attained 98% accuracy, 98.1% recall, and 98.1% precision. It has been discovered that using a custom Mask R-CNN approach on Iris-Retina images improves efficiency and accuracy in person recognition

Nuclear-localized focal adhesion kinase regulates inflammatory VCAM-1 expression.

Vascular cell adhesion molecule-1 (VCAM-1) plays important roles in development and inflammation. Tumor necrosis factor-α (TNF-α) and focal adhesion kinase (FAK) are key regulators of inflammatory and integrin-matrix signaling, respectively. Integrin costimulatory signals modulate inflammatory gene expression, but the important control points between these pathways remain unresolved. We report that pharmacological FAK inhibition prevented TNF-α-induced VCAM-1 expression within heart vessel-associated endothelial cells in vivo, and genetic or pharmacological FAK inhibition blocked VCAM-1 expression during development. FAK signaling facilitated TNF-α-induced, mitogen-activated protein kinase activation, and, surprisingly, FAK inhibition resulted in the loss of the GATA4 transcription factor required for TNF-α-induced VCAM-1 production. FAK inhibition also triggered FAK nuclear localization. In the nucleus, the FAK-FERM (band 4.1, ezrin, radixin, moesin homology) domain bound directly to GATA4 and enhanced its CHIP (C terminus of Hsp70-interacting protein) E3 ligase-dependent polyubiquitination and degradation. These studies reveal new developmental and anti-inflammatory roles for kinase-inhibited FAK in limiting VCAM-1 production via nuclear localization and promotion of GATA4 turnover

Subcutaneous Vein Recognition System Using Deep Learning for Intravenous (IV) Access Procedure

Intravenous (IV) access is an important daily clinical procedure that delivers fluids or medication into a patient’s vein. However, IV insertion is very challenging where clinicians are suffering in locating the subcutaneous vein due to patients’ physiological factors such as hairy forearm and thick dermis fat, and also medical staff’s level of fatigue. To resolve this issue, researchers have proposed autonomous machines to be used for IV access, but such equipment are lacking capability in detecting the vein accurately. Therefore, this project proposes an automatic vein detection algorithm using deep learning for IV access purpose. U-Net, a fully connected network (FCN) architecture is employed in this project due to its capability in detecting the near-infrared (NIR) subcutaneous vein. Data augmentation is applied to increase the dataset size and reduce the bias from overfitting. The original U-Net architecture is optimized by replacing up-sampling with transpose convolution as well as the additional implementation of batch normalization besides reducing the number of layers to diminish the risk of overfitting. After fine-tuning and retraining the hypermodel, an unsupervised dataset is used to evaluate the hypermodel by selecting 10 checkpoints for each forearm image and comparing the checkpoints on predicted outputs to determine true positive vein pixels. The proposed lightweight U-Net has achieved slightly lower accuracy (0.8871) than the original U-Net architecture. Even so, the sensitivity, specificity, and precision are greatly improved by achieving 0.7806, 0.9935, and 0.9918 respectively. This result indicates that the proposed algorithm can be applied into the venipuncture machine to accurately locate the subcutaneous vein for intravenous (IV) procedures

Subcutaneous Vein Recognition System Using Deep Learning for Intravenous (IV) Access Procedure

Intravenous (IV) access is an important daily clinical procedure that delivers fluids or medication into a patient’s vein. However, IV insertion is very challenging where clinicians are suffering in locating the subcutaneous vein due to patients’ physiological factors such as hairy forearm and thick dermis fat, and also medical staff’s level of fatigue. To resolve this issue, researchers have proposed autonomous machines to be used for IV access, but such equipment are lacking capability in detecting the vein accurately. Therefore, this project proposes an automatic vein detection algorithm using deep learning for IV access purpose. U-Net, a fully connected network (FCN) architecture is employed in this project due to its capability in detecting the near-infrared (NIR) subcutaneous vein. Data augmentation is applied to increase the dataset size and reduce the bias from overfitting. The original U-Net architecture is optimized by replacing up-sampling with transpose convolution as well as the additional implementation of batch normalization besides reducing the number of layers to diminish the risk of overfitting. After fine-tuning and retraining the hypermodel, an unsupervised dataset is used to evaluate the hypermodel by selecting 10 checkpoints for each forearm image and comparing the checkpoints on predicted outputs to determine true positive vein pixels. The proposed lightweight U-Net has achieved slightly lower accuracy (0.8871) than the original U-Net architecture. Even so, the sensitivity, specificity, and precision are greatly improved by achieving 0.7806, 0.9935, and 0.9918 respectively. This result indicates that the proposed algorithm can be applied into the venipuncture machine to accurately locate the subcutaneous vein for intravenous (IV) procedures

A review of finger vein recognition system

Recently, the security-based system using finger vein as a biometric trait has been getting more attention from researchers all over the world, and these researchers have achieved positive progress. Many works have been done in different methods to improve the performance and accuracy of the personal identification and verification results. This paper discusses the previous methods of finger vein recognition system which include three main stages: preprocessing, feature extraction and classification. The advantages and limitations of these previous methods are reviewed at the same time we present the main problems of the finger vein recognition system to make it as a future direction in this field