Biometric Person Identification Using Near-infrared Hand-dorsa Vein Images

Biometric recognition is becoming more and more important with the increasing demand for security, and more usable with the improvement of computer vision as well as pattern recognition technologies. Hand vein patterns have been recognised as a good biometric measure for personal identification due to many excellent characteristics, such as uniqueness and stability, as well as difficulty to copy or forge. This thesis covers all the research and development aspects of a biometric person identification system based on near-infrared hand-dorsa vein images. Firstly, the design and realisation of an optimised vein image capture device is presented. In order to maximise the quality of the captured images with relatively low cost, the infrared illumination and imaging theory are discussed. Then a database containing 2040 images from 102 individuals, which were captured by this device, is introduced. Secondly, image analysis and the customised image pre-processing methods are discussed. The consistency of the database images is evaluated using mean squared error (MSE) and peak signal-to-noise ratio (PSNR). Geometrical pre-processing, including shearing correction and region of interest (ROI) extraction, is introduced to improve image consistency. Image noise is evaluated using total variance (TV) values. Grey-level pre-processing, including grey-level normalisation, filtering and adaptive histogram equalisation are applied to enhance vein patterns. Thirdly, a gradient-based image segmentation algorithm is compared with popular algorithms in references like Niblack and Threshold Image algorithm to demonstrate its effectiveness in vein pattern extraction. Post-processing methods including morphological filtering and thinning are also presented. Fourthly, feature extraction and recognition methods are investigated, with several new approaches based on keypoints and local binary patterns (LBP) proposed. Through comprehensive comparison with other approaches based on structure and texture features as well as performance evaluation using the database created with 2040 images, the proposed approach based on multi-scale partition LBP is shown to provide the best recognition performance with an identification rate of nearly 99%. Finally, the whole hand-dorsa vein identification system is presented with a user interface for administration of user information and for person identification

NEFI: Network Extraction From Images

Networks and network-like structures are amongst the central building blocks of many technological and biological systems. Given a mathematical graph representation of a network, methods from graph theory enable a precise investigation of its properties. Software for the analysis of graphs is widely available and has been applied to graphs describing large scale networks such as social networks, protein-interaction networks, etc. In these applications, graph acquisition, i.e., the extraction of a mathematical graph from a network, is relatively simple. However, for many network-like structures, e.g. leaf venations, slime molds and mud cracks, data collection relies on images where graph extraction requires domain-specific solutions or even manual. Here we introduce Network Extraction From Images, NEFI, a software tool that automatically extracts accurate graphs from images of a wide range of networks originating in various domains. While there is previous work on graph extraction from images, theoretical results are fully accessible only to an expert audience and ready-to-use implementations for non-experts are rarely available or insufficiently documented. NEFI provides a novel platform allowing practitioners from many disciplines to easily extract graph representations from images by supplying flexible tools from image processing, computer vision and graph theory bundled in a convenient package. Thus, NEFI constitutes a scalable alternative to tedious and error-prone manual graph extraction and special purpose tools. We anticipate NEFI to enable the collection of larger datasets by reducing the time spent on graph extraction. The analysis of these new datasets may open up the possibility to gain new insights into the structure and function of various types of networks. NEFI is open source and available http://nefi.mpi-inf.mpg.de

Hardware core of pipelined thinning algorithm

The process to associate a particular individual with an identity is known as personal recognition. One of the recognition system type is biometrics system. A biometric system is essentially a pattern recognition system that that performs authentication based on the individual’s behavioural or physiological characteristics. One of the method is finger vein biometrics, which is an authentication technique that identify individuals and verify identity based on the images of human finger veins beneath the skin. There are a lot of process involved in a complete biometrics system. One of the process is thinning. Thinning or skeletonization is a process that extracts the vein patterns from binary image and produces 1-pixel wide output binary image as the result. Existing biometrics system have their own weaknesses and drawbacks such as not showing "aliveness" and also easy to be tampered with. Moreover, software implementation of biometrics system usually performed in an insecure environment and biometrics template stored in a central server. This is insecure and can cause leakage of information. Furthermore, thinning is a time consuming process, which takes a very long time to be completed in software implementation. So the objective of this work is to design a dedicated hardware core for thinning algorithm, implement and enhance the existing algorithm for better hardware performance, and apply pipeline architecture to the hardware design to further speed-up thinning process. This work will implement the algorithm in software and hardware. Hardware implementation of the algorithm is compared with software implementation in terms of accuracy and performance (speed). The hardware core designed managed to achieve a significant improvement in processing time. The work done in this project also managed to map a complex algorithm into hardware implementation and is the first one to implement thinning hardware design using System Verilog

Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network

In fruit production, critical crop management decisions are guided by bloom intensity, i.e., the number of flowers present in an orchard. Despite its importance, bloom intensity is still typically estimated by means of human visual inspection. Existing automated computer vision systems for flower identification are based on hand-engineered techniques that work only under specific conditions and with limited performance. This letter proposes an automated technique for flower identification that is robust to uncontrolled environments and applicable to different flower species. Our method relies on an end-to-end residual convolutional neural network (CNN) that represents the state-of-the-art in semantic segmentation. To enhance its sensitivity to flowers, we fine-tune this network using a single dataset of apple flower images. Since CNNs tend to produce coarse segmentations, we employ a refinement method to better distinguish between individual flower instances. Without any preprocessing or dataset-specific training, experimental results on images of apple, peach, and pear flowers, acquired under different conditions demonstrate the robustness and broad applicability of our method

An Efficient Dorsal Hand Vein Recognition Based on Firefly Algorithm

Biometric technology is an efficient personal authentication andidentification technique. As one of the main-stream branches, dorsal handvein recognition has been recently attracted the attention of researchers. It is more preferable than the other types of biometrics becuse it’s impossible to steal or counterfeit the patterns and the pattern of the vessels of back of the hand is fixed and unique with repeatable biometric features. Also, the recent researches have been obtained no certain recognition rate yet becuse of the noises in the imaging patterns, and impossibility of Dimension reducing because of the non-complexity of the models, and proof of correctness of identification is required. Therefore, in this paper, first, the images of blood vessels on back of the hands of people is analysed, and after pre-processing of images and feature extraction (in the intersection between the vessels) we began to identify people using firefly clustering algorithms. This identification is done based on the distance patterns between crossing vessels and their matching place. The identification will be done based on the classification of each part of NCUT data set and it consisting of 2040 dorsal hand vein images. High speed in patterns recognition and less computation are the advantages of this method. The recognition rate of this method ismore accurate and the error is less than one percent. At the end thecorrectness percentage of this method (CLU-D-F-A) for identification iscompared with other various algorithms, and the superiority of the proposed method is proved.DOI:http://dx.doi.org/10.11591/ijece.v3i1.176

A Bimodal Biometric Student Attendance System

A lot of attempts have been made to use biometrics in class attendance systems. Most of the implemented biometric attendance systems are unimodal. Unimodal biometric systems may be spoofed easily, leading to a reduction in recognition accuracy. This paper explores the use of bimodal biometrics to improve the recognition accuracy of automated student attendance systems. The system uses the face and fingerprint to take students’ attendance. The students’ faces were captured using webcam and preprocessed by converting the color images to grey scale images. The grey scale images were then normalized to reduce noise. Principal Component Analysis (PCA) algorithm was used for facial feature extraction while Support Vector Machine (SVM) was used for classification. Fingerprints were captured using a fingerprint reader. A thinning algorithm digitized and extracted the minutiae from the scanned fingerprints. The logical technique (OR) was used to fuse the two biometric data at the decision level. The fingerprint templates and facial images of each user were stored along with their particulars in a database. The implemented system had a minimum recognition accuracy of 87.83%

A personal identification biometric system based on back-of-hand vein patterns

This report describes research on the use of back-of-hand vein patterns as a means of uniquely identifying people. In particular it describes a prototype biometric system developed by the Australian Institute of Security and Applied Technology (AISAT). This system comprises an infrared cold source, a monochrome CCD camera, a monochrome frame-grabber, a personal computer, and custom image acquisition, processing, registration, and matching software. The image processing algorithms are based on Mathematical Morphology. Registration is performed using rotation and translation with respect to the centroid of the two-dimensional domain of a hand. Vein patterns are stored as medial axis representations. Matching involves comparing a given medial axis pattern against a library of patterns using constrained sequential correlation. The matching is two-fold: a newly acquired signature is matched against a dilated library signature, and then the library signature is matched against the dilated acquired signature; this is necessary because of the positional noise exhibited by the back-of-hand veins. The results of a cross-matching experiment for a sample of 20 adults and more than 100 hand images is detailed. In addition preliminary estimates of the false acceptance rate (FAR) and false rejection rate (FRR) for the prototype system are given. Fuzzy relaxation on an association graph is discussed as an alternative to sequential correlation for the matching of vein signatures. An example is provided (including a C program) illustrating the matching process for a pair of signatures obtained from the same hand. The example demonstrates the ability of the fuzzy relaxation method to deal with segmentation errors